Table 1.
Summary of empirical findings on climate change and health expenditures
| Authors | Country | Period | Estimation Technique | Major Findings |
|---|---|---|---|---|
| Shahzad et al. (2020) | Pakistan | 1995–2017 | ARDL, VECM, FMOLS, DOLS, CCR | Growth and CO2 emissions have a positive while RE and ICT have a negative impact on health expenditure. |
| Ullah et al. (2019a) | Pakistan | 1998–2017 | 2SLS, 3SLS, Pairwise Granger Causality test | Trade increases CO2, CO2 increases HE. RE has a negative effect on HE and CO2. HE and GDP have bi-directional causality. |
| Apergis et al. (2018a) | 42 Sub-Saharan African countries | 1995–2011 | Pedroni’s panel co-integration, FMOLS, DOLS, VECM | In SR granger causality runs from real GDP to CO2, RE, HE. In LR, unidirectional causality runs from RE to HE, and bi-direction between HE and CO2. HE and RE decrease while real GDP increases carbon emission. |
| Khan et al. (2016) | Selected developed countries | 2000–2013 | GMM | EKC confirmed, U shape relation between PFC and Pm2.5 with per capita income, CO2 increases HE; Energy demand increases HE; inverted u shape relationship among per capita health expenditure and GDP. |
| Zaidi and Saidi (2018) | Sub Saharan African | 1990–2015 | ARDL, VECM | In LR, EG positively impacts HE, CO2, and Nitrous oxide have a negative impact on HE. In SR, causality runs from HE to GDPPC and two-way causality among CO2, GDP, and HE and CO2. |
| Wu et al. (2020) | Taiwan | 1995Q1–2016Q4 | Wavelet analysis | There is unidirectional causality from HE per capita to CO2 emission per capita. |
| Alimi et al. (2019) | 15 ECOWAS | 1995–2014 | Pooled OLS, FE, System GMM | Carbon emission has a positive effect on public HE, but no effect on private HE. |
| Abdullah et al. (2016) | Malaysia | 1970–2014 | ARDL, ECM | CO2, SO2, NO2 have a negative effect on HE in LR, but a positive effect in SR.GDP, FR, MR effects HE negatively in LR and SR. |
| Yu et al. (2016) | 31 Chinese provinces | 1997–2014 | FMOLS, Panel based ECM |
In LR waste gas, dust and smog, and water waste increase HE. In SR, Dust and Smog are insignificant, Waste gas and GDPPC significantly affect HE. |
| Yazdi and Khanalizadeh (2017) | MENA countries | 1995–2014 | ARDL | CO2 and PM10 have a positive effect on HE in LR. |
| Yahaya et al. (2016) | 125 Developing country | 1995–2012 | Panel OLS, and Panel DOLS |
In LR; CO, NOX, CO2, NO, Y IN SR; SO2 and NO are insignificant; Y, CO, and CO2 contribute positively to HE. |
| Blázquez-Fernández et al. (2019) | 29 OECD countries | 1995–2014 | Dynamic panel data | GDPPC has a positive effect on HE. Sulfur contributes to HE in linear and Carbon monoxide in the dynamic model. |
| Narayan and Narayan (2008) | 8 OECD countries | 1980–1999 | Panel OLS and DOLS |
In LR, SO, CO, and GDPPC are significant and positive, while NO is insignificant. In SR; GDPPC and CO are significant but SO and NO are not. |
| Chaabouni et al. (2016) | A global panel of 51 countries | 1995–2013 | GMM | There is a unidirectional causal relationship from CO2 to HE with an exception for low-income countries. For low-income countries, there is bidirectional causality. |
| Moosa and Pham (2019) | 8 country groups | 1995–2015 | FMOLS | GDPPC is more important in determining HE for Low and middle-income countries than Environment, however, in high-income countries both are important. |
| Badulescu et al. ( 2019) | 28 EU countries | 2000–2014 | ARDL |
In LR, CO2, GDPPC contributes positively and Renewable decreases HE. In SR, all results are the same, however, Renewable is not significant. |
| Chaabouni and Saidi (2017) | 3 groups of 51 countries | 1995–2013 | GMM, Dynamic Simultaneous Equation Model | Bidirectional causality between GDP and CO2, and between HE and GDP. While unidirectional from CO2 to HE except for low-income countries. |
| Farooq et al. (2019) | 30 Chinese provinces | 1996–2015 | Quantile regression | CO2 and population contribute positively, Afforestation negatively in HE. NO2 and SO2 have mixed results for various quintiles. Gas and oil consumption has no significant impact on Health. |
| Erdoğan et al.(2019) | Turkey | 1971–2016 | Johansen co-integration, DOLS | In LR, 1% increase in CO2 decreases life exp. by 0.14 and increases infant mortality rate by 0.19 %. |
| Ullah et al. (2019b) | China | 1990–2017 | 2SLS, 3SLS, Granger Causality |
CO2 and Population increase HE, Trade, and Industrial production do not affect HE. Uni-directional causality runs from CO2 to HE. Bi-directional causality between Trade and HE and Population growth and HE. |
| Metu et al. (2017) | Nigeria | 1990–2015 | ARDL |
In LR; Total GHG affects negatively HE, while all other variables are positive and significant. In SR; GHG is again negative, Population density and infant mortality contribute positively. |
| Yahaya et al. (2016) | 125 Developing countries | 1995–2012 | OLS, DOLS |
In LR, CO2, CO, SO2, No, GDPPC, all contribute positively. After GDP, CO2 and CO have the highest impact on HE. In SR, all variables show the same significance and sign except SO2 and NO. |
| Abdullah et al. (2016) | Malaysia | ARDL |
In LR, CO2, CO, SO2, NO, and GDPPC affect positively HE. In SR, CO2, SO2 shows positive while MR and FR show negative signs. |
|
| Khan et al. (2019) | 58 Belt and Road Initiative Countries | 1995–2016 | GMM, FMOLS | CO2 is positively associated with HE, GDP, and negatively with FDI and RE in LR. |
| Zaman and Abd-el Moemen (2017) | 14 Latin American and Caribbean countries | 1980-2013 | FE, RE, 2SLS | Electricity, permanent cropland and HE increase CO2. High technology exports reduce CO2. |
| Apergis et al. (2018b) | 50 U.S state-level data | 1996–2009 | Quintile Regression | Income increases health expenditures; CO2 increases health expenditures. |
| Murthy and Okunade (2016) | USA | 1960–2012 | ARDL Bound test | USA health care system is a necessity with income elasticity found to be 0.92 |
| Khandelwal (2015) | India | 1971–2011 | ARDL, VECM |
In LR, HE is significantly affected by GDP, Fiscal deficit, and Energy In SR, the only GDP has a relation with HE |
| Xing et al. (2019) | 33 countries with high GDP | 1995–2015 | FE | GHG and Pm10 are negatively associated with Life expectancy after a certain level of pollution emission scale. |
| Xu et al. (2019) | China 30 provinces | 2005–2016 | Quantile regression | Waste and Gas have a positive effect on HE in high and medium-income regions and a negative in the low-income region. Income has a positive effect across all five quintiles. |
| Usman et al. (2019) | 13 emerging economies | 1994–2017 | CUP-FM, CUP-BC | CO2 and index have a positive effect on govt. HE, but negative on private HE, GDP has a positive effect on both govt. and private HE. The aging population increases HE; secondary education decreases private HE. |
LR long run, SR short run, HE health expenditures, CO2 carbon dioxide emissions, SO2 sulfur dioxide, NO nitrous oxide, CO carbon mono oxide, WW water waste, GHG greenhouse gases, FE fixed effect, RE random effect, CUP-FM continuously updated-fully modified, CUP-BC continuously updated-bias-corrected, GDPPC gross domestic product per capita, EKC environmental Kuznets curve