Using annual time-series data over the period 1975–2017, the researcher applied the bootstrap autoregressive-distributed lag (ARDL) cointegration model developed by McNown et al. (1) to examine whether there is a long run relationship among health expenditure, CO₂ emissions, and gross domestic product (GDP) per capita in 18 Organization for Economic Cooperation and Development (OECD) countries. We find cointegration exists in Netherlands when real GDP per capita serves as a dependent variable, in New Zealand when health expenditure is the dependent variable, and in the United States when CO₂ emissions are dependent variables. The main results show evidence of a short run relationship between the three variables. The empirical results support that there is a bidirectional causality between health expenditure and GDP growth for Germany and the United States, between CO₂ emissions and GDP growth for Canada, Germany, and the United States, and between health expenditure and CO₂ emissions for New Zealand and Norway. The results also indicate that there are unidirectional causality in other countries.

This paper analyzes the co-movement and causal linkages between environmental pollution and healthcare expenditure, taking economic growth as a control variable by using wavelet analysis for Taiwan over the period 1995 Q1–2016 Q4. The results show that there exists co-movement and causality between environmental pollution and healthcare expenditure at different frequencies and times. The changes in the relationships of the two variables are observed in certain events such as the period of the expansion stage, the policy of environmental pollution, and the issue of the National Health Insurance Integrated Circuit card (NHI-IC) in Taiwan. In the short-term, positive causality runs from healthcare expenditure to environmental pollution before 2004, while negative causality runs from healthcare expenditure to environmental pollution before 2007 in the long-term. After adding economic growth as a control variable, positive causality runs from healthcare expenditure to environmental pollution in the period 2009–2011 in the short-term, while negative causality running from healthcare expenditure to environmental pollution is shown in 2008 in the long-term. The results indicate that “higher government health expenditure leading to higher demand for environment quality” exists in different sub-periods and the argument may concern the factor of economics in the long-term. The positive healthcare lead in the short-term may be based on economics in the expansion stage. Also, the issue of NHI-IC possibly affects the dynamic relationship between healthcare expenditure and environmental pollution without considering economics. Based on empirical analysis, certain policy and managerial implications are addressed for decision-makers at macroeconomic and microeconomic levels.

The influence of low-carbon energy on economic development is a vital issue. Using the provincial panel data in China from 2000 to 2017, this work investigated the aggregate effects of low-emission electricity. The results showed that 1) when the ratio of low-emission electricity to total electricity increases by 1%, the GDP per capita will increase by 0.16% and CO₂ emissions will decrease by 0.848%. In other words, low-emission electricity can achieve the goal of low-carbon economic development; 2) the self-supply of low-emission electricity, rather than trade and efficiency, is the main reason for China’s boosted economic growth; and 3) low-emission electricity increases the regional economic gap in China. The effects of pollution inhibition and economic promotion on low-emission electricity in developed areas are significantly greater than those in less developed areas. Thus, the low-emission electricity policy in China should benefit the economy and avoid the excessive economic gap among regions. Policymakers should vigorously promote the low-emission electricity revolution and pay attention to the inclination of energy policy to the central and western regions.

This paper investigates the causal relationship between water pollution, industrial agglomeration, and economic growth in 11 Chinese provinces in China. Using a bootstrap autoregressive distributed lag (ARDL) analysis, we examine the estimated models’ stability and investigate the Granger causality relationships between system variables. The results indicate that our estimation provides evidence of a long-run relationship between water pollution, industrial agglomeration, and economic growth in China. The bidirectional running from other water pollution variables is confirmed from the casualty tests in Shanghai and Yunnan. Specifically, the unidirectional causality relationship running from industrial agglomeration and economic growth to water pollution provide statistical evidence of the important role that industrial agglomeration and economic growth play in increasing water pollution.