Statistical Analysis and Prediction of Fatal Accidents in the Metallurgical Industry in China

Self Cite

Background: Construction activities not only provide the necessary conditions for citizens to live, but also cause fatal accidents. Methods: This study aimed to reveal the characteristics of fatal accidents in the construction industry in China based on statistical data. From 2010 to 2019, there were 6005 fatal accidents in China’s construction industry causing 7275 deaths. The important features of these fatal accidents, such as the type, time of occurrence, site location, severity, and geographical region of the accident, were carefully analyzed. Results: There were 258 major and severe construction accidents causing 1037 deaths, accounting for 4.3% and 14.25% of the total number of construction accidents and deaths in this period, respectively. As an important finding, more deaths occurred in August and on Mondays. The greatest number of construction accidents took place along openings and edges, accounting for 22.9% of all fatal accidents. Taking into account their economic development level and number of employees, Qinghai and Hainan experienced a higher mortality rate than Jiangsu. Falls from a high place were the dominant type of construction accident, accounting for 51.66% of all accidents. However, collapses were the primary type of major and severe construction accident, accounting for 60.09% of such accidents. The predicted number of construction deaths in 2020 is 887 according to the GM(1,1) model. Corresponding safety measures should be adopted to improve the working environment of the construction industry. Implications: The implications of these results with respect to the characteristics of construction accidents can be regarded as the foundation for accident prevention in practice.

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Analysis of the Characteristics of Fatal Accidents in the Construction Industry in China Based on Statistical Data

2021

Self Cite

“…Social functioning is highly complex and uncertain, and the various statistical data representing social functioning may be grey due to human or technical problems. The study of the environmental aspects has been carried out in a number of research areas, including the following: the assessment of the environmental impact of the development of the environment [ 21 ], the assessment of satisfaction [ 37 ], the assessment of ecological vulnerability [ 38 ], the evaluation of safety incidents [ 39 ], and the assessment of air pollution [ 40 ]. The use of a multi-level grey system evaluation method in measuring climate resilience can overcome the gaps in existing studies and provide a more scientifically valid assessment of the climate resilience of regions.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessment of the Construction of a Climate Resilient City: An Empirical Study Based on the Difference in Differences Model

Liang

2021

Facing climate risks has become a common problem for mankind and a topic of great importance for the Chinese government. To thoroughly implement the overall requirements for the construction of an ecological civilization and effectively improve the capacity of cities to adapt to climate change, China launched the pilot construction of “Climate Resilient Cities” in 2017. In this paper, 16 prefecture level cities in Anhui Province of China were selected as the research objects, and the multi-level grey system evaluation method was used to measure the climate resilience of these regions. We used the difference in differences method to evaluate the effect of the pilot policy of “Climate Resilient Cities.” The pilot policies of the “Climate Resilient Cities” showed a significant contribution to the regional climate resilience, and, after isolating the impact of other factors on the regional climate resilience, the pilot policies of the “Climate Resilient Cities” increased the climate resilience of the pilot cities by four percentage points. The pilot policies of the “Climate Resilient Cities” had a significant contribution to the urban infrastructure development and ecological space optimization, as well as non-significant impacts to the urban water security, emergency management capacity-building, and science and technology innovation initiatives.

“…In addition, the hybrid model overcomes the defects of using only a single indicator and low accuracy of traditional models, and the purpose of using the GM (1, 1) as a predictive model is its simplicity, high accuracy, and no need for typical probability distributions [ 35 ]. Compared with similar studies [ 36 , 37 ], this article is based on the double test of residual error and posterior error to improve the accuracy of ecological security early-warning value and model accuracy. The study emphasizes the importance of alpine agricultural mountainous areas to the development of ecological security, and provides references for comprehensive management of the local ecological environment and improving the sustainability of land use and ecological protection policies.…”

Section: Introductionmentioning

confidence: 99%

Early-Warning Measures for Ecological Security in the Qinghai Alpine Agricultural Area

Guo

Zhen

Ren

et al. 2020

The study area of this paper is the Qinghai alpine agricultural mountain area. An ecological security early-warning model is used to identify the early warning signs of ecosystem destruction, environmental pollution and resource depletion in districts and counties from 2011 to 2018. A combination of qualitative and quantitative early-warning models is used to predict the existence of hidden or sudden advance warnings. The grey (1, 1) model (GM) is used to predict the evolution trend of ecological security warning situations from 2019 to 2021. On this basis, GIS technology is used to analyze the spatial pattern changes in three periods. The results show that from 2011 to 2018, the ecological environment in Qinghai’s alpine agricultural mountainous area gradually improved. In 2018, the ecological security early-warning values of all districts and counties were greater than the 2011 values. However, in 2018, the ecological security early-warning levels of PA, LD and HZh (PA, LD and HZh refer to Ledu District, Ping’an District and Huzhu Tu Autonomous County respectively.) were in the “good” ecological early-warning state, while the ecological security levels of other cities were still in the “moderate” or “mild” ecological warning state. According to the prediction results, the early-warning level of ecological security in Qinghai’s alpine agricultural mountainous areas will improve further in 2021, with the “good” states dominating. From a spatial perspective, the ecological environment in the northeast region is better than that in the southern region, and the internal differences in the ecological security early-warning levels tend to narrow. Thus, we propose that areas with different ecological security levels should focus on the management and protection of the ecological environment or carry out ecological restoration or reconstruction. The aim of this paper is to provide a reference for the improvement of the ecological environment in general and the sustainable development of the economy and society as well as the ecological environment of alpine agricultural mountainous areas in particular.