Does environment quality and public spending on environment promote life expectancy in China? Evidence from a nonlinear autoregressive distributed lag approach

Shah, Muhammad Haroon; Wang, Nianyong; Ullah, Irfan; Akbar, Ahsan; Khan, Karamat; Bah, Kebba

doi:10.1002/hpm.3100

Cited by 22 publications

(29 citation statements)

References 56 publications

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“…In addition, some studies have shown that the pursuit of economic development in developing countries can have an adverse impact on the environment, such as industrial pollution, indirectly affecting the life expectancy of local residents [6,26,27]. However, environmental expenditure resulting from the accumulation of economic capacity can increase life expectancy [28].…”

Section: Research On the Impact From Economic Development Level On Life Expectancymentioning

confidence: 99%

Impacts from Economic Development and Environmental Factors on Life Expectancy: A Comparative Study Based on Data from Both Developed and Developing Countries from 2004 to 2016

Zhi-heng

Hua

et al. 2021

IJERPH

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Both economic development level and environmental factors have significant impacts on life expectancy at birth (LE). This paper takes LE as the research object and selects nine economic and environmental indicators with various impacts on LE. Based on a dataset of economic and environmental indicators of 20 countries from 2004 to 2016, our research uses the Pearson Correlation Coefficient to evaluate the correlation coefficients between the indicators, and we use multiple regression models to measure the impact of each indicator on LE. Based on the results from models and calculations, this study conducts a comparative analysis of the influencing mechanisms of different indicators on LE in both developed and developing countries, with conclusions as follow: (1) GDP per capita and the percentage of forest area to land area have a positive impact on LE in developed countries; however, they have a negative impact on LE in developing countries. Total public expenditure on education as a percentage of GDP and fertilizer consumption have a negative impact on LE in developed countries; however, they have a positive impact on LE in developing countries. Gini coefficient and average annual exposure to PM2.5 have no significant effect on LE in developed countries; however, they have a negative impact on LE in developing countries. Current healthcare expenditures per capita have a negative impact on LE in developed countries, and there is no significant impact on LE in developing countries. (2) The urbanization rate has a significant positive impact on LE in both developed countries and developing countries. Carbon dioxide emissions have a negative impact on LE in both developed and developing countries. (3) In developed countries, GDP per capita has the greatest positive impact on LE, while fertilizer consumption has the greatest negative impact on LE. In developing countries, the urbanization rate has the greatest positive impact on LE, while the Gini coefficient has the greatest negative impact on LE. To improve and prolong LE, it is suggested that countries should prioritize increasing GDP per capita and urbanization level. At the same time, countries should also work on reducing the Gini coefficient and formulating appropriate healthcare and education policies. On the other hand, countries should balance between economic development and environmental protection, putting the emphasis more on environmental protection, reducing environmental pollution, and improving the environment’s ability of self-purification.

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Section: Research On the Impact From Economic Development Level On Life Expectancymentioning

confidence: 99%

Impacts from Economic Development and Environmental Factors on Life Expectancy: A Comparative Study Based on Data from Both Developed and Developing Countries from 2004 to 2016

Zhi-heng

Hua

et al. 2021

IJERPH

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“…Besides, industrial production heavily relies on fossil fuels, which are the biggest contributor to environmental pollution, leading to adversely affected human health. 5 The study conducted by Ostro and Rothschild 6 revealed that small particulate matters may result in work loss and even lead to bed disability in adults. Schwartz and Dockery 7 reported that contaminants, such as total suspended particulate (TSP) and sulfur dioxide, increase human death chances.…”

Section: Introductionmentioning

confidence: 99%

Public Health Expenditures and Health Outcomes in Pakistan: Evidence from Quantile Autoregressive Distributed Lag Model

Ullah¹,

Ullah²,

Ali³

et al. 2021

RMHP

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Objective The provision of healthcare facilities remains high on the manifesto of various political parties in Pakistan and healthcare spending has witnessed a significant surge in the last two decades that is expected to positively influence health outcomes in the country. Therefore, this research aims to explore the effects of healthcare expenditures on the actual health status of the masses in Pakistan for the period 1995Q1 to 2017Q1. Methods We apply the Quantile Autoregressive Distributed Lag (QARDL) approach for estimation purposes. This is the most recent and emerging estimation technique in time series analysis. Results Our findings confirm that public healthcare spending significantly impacts health outcomes in Pakistan both in the short-run and long-run. Public healthcare spending improves life expectancy and reduces death rate and infant mortality. Conclusion The study concludes that public healthcare is the main focus of the current regime. It is noticed that spending on healthcare significantly contributes to the health outcomes in Pakistan. These efforts by the government significantly promote life expectancy and drop down the mortality ratio in the country. Based on these notable facts, the government should allocate sufficient resources towards the latest healthcare technologies and equipment to optimize health outcomes in the country.

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“…(2015) employed the first-difference model to conclude that exposure to air particulate significantly decreases life expectancy in China propelled by high rate of cardiorespiratory mortality, the findings departs sharply from those of Dockery (2009) for the United States. Also, Shah et al. (2021) adopted the nonlinear autoregressive distributed lag (ARDL) method to analyse quarterly data from 1999 to 2017, and reported a long-run negative relationship between environmental pollution and life expectancy in China.…”

Section: Literature Reviewmentioning

confidence: 99%

Air pollution trajectories and life expectancy in Nigeria

Nwani

2022

IJSE

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PurposeThe purpose of the study is to examine the relationship between air pollution and life expectancy considering the roles of institutional quality, agricultural output, foreign direct investment (FDI) and other socio-economic variables in Nigeria from 1981Q1 to 2019Q4.Design/methodology/approachThe study employed spliced quarterly data from annual series collected from the World Bank development indicators and Central Bank of Nigeria. The dynamic multivariate models were analysed using the vector error correction mechanism (VECM), variance decomposition and Granger causality techniques.FindingsThe VECM result indicated a statistically significant adverse effect of air pollution on life expectancy. However, institutional quality, gross domestic product per capita, agricultural output, government social expenditure and school enrolment rate ameliorate the adverse health effects of air pollution, while FDI had mixed effects on life expectancy at different significance levels and at varying lag lengths. The Granger causality result revealed a uni-directional causality from air pollution to life expectancy; bidirectional causal chain between agriculture, FDI, government social expenditure and life expectancy, while a uni-directional causal linkage run from life expectancy to income per head and from school enrolment to life expectancy respectively. However, there is no evidence of causation between institutional quality and life expectancy due to weak institutional quality, but foreign direct invest causes carbon emission in a uni-directional manner in line with pollution haven hypothesis.Research limitations/implicationsThe study's modelling is limited by not considering the resource curse variable in the model due to paucity of data. Nigeria is the biggest crude oil exporter in Africa and ranks 13th globally with daily output of about 2.4 m barrels. Thus, the negation of resource curse in air pollution–life expectancy nexus de-emphasises the effectiveness of rich resources on health and environment. Future studies could address this limitation by incorporating resource curse in environmental-health models for Nigeria.Practical implicationsIt is imperative for the country to adopt stringent anti-air pollution strategies that would establish a balance between FDI attraction and agricultural expansion to the benefits of her citizens' longevity. Also, education should be considered as a strategic action to enhance life expectancy through expansion in the provision, accessibility and affordability to improved school enrolment rate. The choice of quarterly time series over annual data helped to establish the current relationship between air pollution and life expectancy using efficient estimators.Originality/valueThe study contributes to literature by disaggregating yearly series into quarterly series, which has implications for the efficiency of the estimates, unlike earlier studies which ignored this fundamental process. The result of this study produced reliable policy direction for improvement in life expectancy in an emerging economy since quarterly estimates are more robust and reliable for forecasting than its yearly counterpart. The outcome of the study extended the original tenets of the Grossman's health stock theory using the environmental Kuznets curve (EKC) and pollution haven hypotheses (PHH).

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Does environment quality and public spending on environment promote life expectancy in China? Evidence from a nonlinear autoregressive distributed lag approach

Cited by 22 publications

References 56 publications

Impacts from Economic Development and Environmental Factors on Life Expectancy: A Comparative Study Based on Data from Both Developed and Developing Countries from 2004 to 2016

Impacts from Economic Development and Environmental Factors on Life Expectancy: A Comparative Study Based on Data from Both Developed and Developing Countries from 2004 to 2016

Public Health Expenditures and Health Outcomes in Pakistan: Evidence from Quantile Autoregressive Distributed Lag Model

Air pollution trajectories and life expectancy in Nigeria

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