Can energy policies affect the cycle of carbon emissions? Case study on the energy consumption of industrial terminals in Shanghai, Jiangsu and Zhejiang

Wang, Lihong; Gong, Zaiwu; Gao, Ge; Wang, Changkai

doi:10.1016/j.ecolind.2017.07.036

Cited by 21 publications

(6 citation statements)

References 34 publications

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“…Grey correlation analysis is a quantitative description and comparative method for investigating trends within a system [ 29 , 30 ]. Grey correlation analysis has many advantages: the amount of data required is not large, therefore grey correlation analysis has advantage in dealing with small data samples; a strict calibration method is not required for Grey correlation analysis, so it is simpler and easier to use than other statistical methods; the grey relation results are also more intuitionistic and understandable.…”

Section: Methodsmentioning

confidence: 99%

Correlation Analysis of PM10 and the Incidence of Lung Cancer in Nanchang, China

Zhou

Hu³

2017

IJERPH

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Air pollution and lung cancer are closely related. In 2013, the World Health Organization listed outdoor air pollution as carcinogenic and regarded it as the most widespread carcinogen that humans are currently exposed to. Here, grey correlation and data envelopment analysis methods are used to determine the pollution factors causing lung cancer among residents in Nanchang, China, and identify population segments which are more susceptible to air pollution. This study shows that particulate matter with particle sizes below 10 micron (PM10) is most closely related to the incidence of lung cancer among air pollution factors including annual mean concentrations of SO2, NO2, PM10, annual haze days, and annual mean Air Pollution Index/Air Quality Index (API/AQI). Air pollution has a greater impact on urban inhabitants as compared to rural inhabitants. When gender differences are considered, women are more likely to develop lung cancer due to air pollution. Smokers are more likely to suffer from lung cancer. These results provide a reference for the government to formulate policies to reduce air pollutant emissions and strengthen anti-smoking measures.

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Section: Methodsmentioning

confidence: 99%

Correlation Analysis of PM10 and the Incidence of Lung Cancer in Nanchang, China

Zhou

Hu³

2017

IJERPH

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“…Wong et al [40] came up with a grey correlation test method searching changing points, and the Shunde river network area is taken as an example. Zhang and Gong [41] used the time series example of the agricultural disaster area in eastern China to verify the practicability and strength of the grey correlation algorithm; Chen and Gong [42] identified CO 2 emission trends' change points and cycles from China's energy consumption; and Wang et al [43] calculated the change points of cumulative CO 2 emission from 1995 to 2004 in three eastern China jurisdictions and performed cycle division.…”

Section: Gra-cpmentioning

confidence: 99%

Complex PM_2.5 Pollution and Hospital Admission for Respiratory Diseases over Big Data in Cloud Environment

Zhou

2020

Complexity

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With the establishment of China's national air quality monitoring network, large amounts of monitoring data are available for different kinds of users. How to process and use this big data is a tough problem for users: most users have limited computing power, and new data are collected at every moment. Cloud computing may be an efficient and low-cost way to solve this problem.is paper investigates a problem of a complex system: the impact of PM 2.5 on hospitalization for respiratory diseases. A changepoint detection method based on grey relation analysis was used to solve this problem. Daily air pollution monitoring data and patient data were used in this study. Our results showed that (1) PM 2.5 pollution showed a positive correlation on hospital admission for respiratory disease; (2) most patients went to hospital 2 days after PM 2.5 pollution events; and (3) male, children, and old people were significantly affected by PM 2.5 pollution. Our study is of great significance to help the government formulate suitable policies to reduce the damage caused by PM 2.5 pollution and help hospitals allocate medical resources efficiently.

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“…Second, we fully consider the heterogeneity for China's different provinces. The prior studies consider EC either in whole country ( 1 , 26 – 32 ) or certain one province such as Beijing ( 3 ), Guangdong ( 33 ), Zhejiang ( 34 ), and Shaanxi ( 35 ). Even though these studies widely investigated EC, they did not look at the heterogeneous effects.…”

Section: Introductionmentioning

confidence: 99%

Energy Consumption and Health Insurance Premiums in China's Provinces: Evidence From Asymmetric Panel Causality Test

Wang

Liu

Lobonţ

et al. 2021

Front. Public Health

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This article employs asymmetric panel causality test to address the causal nexus between energy consumption (EC) and healthcare insurance premiums (IP) for China's different provinces. The empirical results indicate that there exist asymmetric causality runs from positive EC shocks to positive healthcare IPs in Beijing, Hebei, Tianjin, Shanxi, Inner Mongolia, Shaanxi, Gansu, Qinghai, Ningxia, Shandong, Henan, and Anhui. There is no significant link in southern and northeastern provinces mainly because of their industrial structure, high economic development level, strong insurance consciousness, and climate conditions. Therefore, governments should encourage technological innovation and further improve energy efficiency. Meanwhile, we need to optimize EC structure and raise the proportion of renewable energies. The authorities should carry out stricter environmental protection policies and protect people from pollution that comes from fossil fuel burn. The commercial health insurance should be included in the health system and become an important supplement to public health insurance.

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Can energy policies affect the cycle of carbon emissions? Case study on the energy consumption of industrial terminals in Shanghai, Jiangsu and Zhejiang

Cited by 21 publications

References 34 publications

Correlation Analysis of PM10 and the Incidence of Lung Cancer in Nanchang, China

Correlation Analysis of PM10 and the Incidence of Lung Cancer in Nanchang, China

Complex PM_2.5 Pollution and Hospital Admission for Respiratory Diseases over Big Data in Cloud Environment

Energy Consumption and Health Insurance Premiums in China's Provinces: Evidence From Asymmetric Panel Causality Test

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Can energy policies affect the cycle of carbon emissions? Case study on the energy consumption of industrial terminals in Shanghai, Jiangsu and Zhejiang

Cited by 21 publications

References 34 publications

Correlation Analysis of PM10 and the Incidence of Lung Cancer in Nanchang, China

Correlation Analysis of PM10 and the Incidence of Lung Cancer in Nanchang, China

Complex PM2.5 Pollution and Hospital Admission for Respiratory Diseases over Big Data in Cloud Environment

Energy Consumption and Health Insurance Premiums in China's Provinces: Evidence From Asymmetric Panel Causality Test

Contact Info

Product

Resources

About

Complex PM_2.5 Pollution and Hospital Admission for Respiratory Diseases over Big Data in Cloud Environment