Revisiting the Existence of EKC Hypothesis under Different Degrees of Population Aging: Empirical Analysis of Panel Data from 140 Countries
Abstract
:1. Introduction
2. Literature Review
2.1. Research on the Relationship between Economic and Environmental Pressure
2.2. Research on the Relationship between GDP and Ecological Footprint
2.3. Summary of Literature
3. Materials and Methods
3.1. Variable Selection and Data Descriptive Analysis
- (1)
- Explained variable: The Ecological Footprint Index is an index used to measure the geographical area required for local biological production and human activities [41], which are provided by the dataset, in units of global hectares. Since this indicator takes into account multiple dimensions such as species diversity and ecological degradation, it is regarded as a reliable indicator for assessing the sustainable development of a region [42].
- (2)
- Explanatory variable: GDP per capita is an indicator that reflects the state of economic development from the perspective of social macroeconomic operation. This calculation did not deduct asset depreciation or natural resource depletion and degradation. The data of per capita GDP are selected from the World Bank, with constant 2010 US$ as the unit.
- (3)
- Threshold variable: Population aging (AG) is the percentage of the population aged 65 and over to the total population, which is selected from the World Bank, and the population is determined according to the actual population definition.
- (4)
- Control variable: Industrial value added (IND) reflects the net results obtained by social industrial enterprises after all social production activities, selected from the World Bank, with constant 2010 US$ as the unit. Urban population to total population (URB): the unit is % of total population. Trade (TR) reflects the degree to which a region’s commodities are opened to the outside world. The unit is % of GDP.
3.2. Construction of Threshold Regression Model
3.3. Threshold Regression of Panel Data
4. Empirical Study
4.1. Data Preprocessing
4.1.1. Descriptive Analysis
4.1.2. Unit Root Test
4.2. Cross-Sectional Dependence Analysis of 140 Countries
4.2.1. Threshold Effect Test
4.2.2. Threshold Panel Regression Results
4.3. Heterogeneity Analysis of Four Income Groups
4.3.1. Threshold Effect Test
4.3.2. Threshold Panel Regression Results
5. Results Discussion
5.1. Discussion of Global Panel Regression Results for 140 Countries
5.2. Discussion of Panel Regression Results by Different Income Groups
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
Abbreviations
EKC | Environmental Kuznets Curve |
R&D | Research and Development |
LLC | Levin-Lin-Chu test |
IPS | Im-Pesaran-Shin test |
ARDL | Autoregressive distributed lagged model |
CO2 | Carbon emissions |
HI | High income group |
UMI | Upper middle income group |
BRICs | Brazil, Russia, India, China |
EF | Ecological footprint |
GDP | Gross Domestic Product |
AG | Population Aging degree |
IND | Industry value added |
URB | Urban population |
MT | Merchandise trade |
LMI | Lower middle income |
LI | Low income |
G7 | Group of Seven |
Appendix A
Income Group Category | Countries |
---|---|
High income | ARE; AUS; AUT; BEL; CHE; CHL; CYP; CZE; DEU; DNK; ESP; EST; FIN; FRA; GBR; GRC; HRV; HUN; IRL; ISR; ITA; JPN; LTU; LUX; LVA; MUS; NLD; NOR; NZL; PAN; POL; PRT; ROU; SAU; SGP; SVK; SVN; SWE; TTO; URY; USA |
Upper-middle income | ALB; ARG; AZE; BGR; BLR; BRA; BWA; CHN; COL; CRI; CUB; DOM; ECU; FJI; GAB; GEO; GRD; GTM; GUY; IDN; IRN; IRQ; JAM; JOR; KAZ; LBN; LCA; MEX; MKD; MYS; PER; PRY; RUS; SUR; THA; TON; TUR; VEN; WSM; ZAF |
Lower-middle income | AGO; BEN; BGD; BOL; BTN; CMR; COG; COM; CPV; DZA; EGY; HND; IND; KEN; KGZ; KHM; LAO; LKA; LSO; MAR; MDA; MMR; MNG; MRT; NGA; NIC; NPL; PAK; PHL; SEN; SLV; STP; SWZ; TLS; TUN; TZA; UKR; UZB; ZMB; ZWE |
Low income | BDI; BFA; COD; ETH; GIN; GMB; GNB; HTI; MDG; MLI; MOZ; MWI; NER; RWA; SLE; TGO; TJK; UGA; YEM |
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Variable Type | Variable Name | Abbreviation | Data Sources |
---|---|---|---|
Explained variable | Ecological footprint | EF | Data.world |
Explanatory variable | GDP per capita | GDP | World Bank |
Threshold variable | Aging degree | AG | World Bank |
Control variable | Industry value added | IND | World Bank |
Urban population | URB | World Bank | |
Merchandise trade | MT | World Bank |
Variables | Mean | Sd | Min | Median | Max |
---|---|---|---|---|---|
LN_EF | 16.9886 | 1.7928 | 12.0760 | 16.9489 | 22.3834 |
AG | 7.8836 | 5.3988 | 0.6856 | 5.6422 | 26.0193 |
LN_GDP | 8.3932 | 1.4879 | 5.2723 | 8.3318 | 11.6260 |
LN_IND | 23.1171 | 2.4106 | 16.6347 | 22.9901 | 29.0568 |
LN_TR | 4.0313 | 0.5163 | 2.0549 | 4.0160 | 5.8391 |
LN_URB | 3.9067 | 0.4934 | 2.1097 | 4.0388 | 4.6052 |
Correlation | LN_GDP | LN_IND | LN_TR | LN_URB |
---|---|---|---|---|
LN_GDP | / | 0.624787 | 0.272504 | 0.657178 |
LN_IND | 0.624787 | / | 0.000737 | 0.580836 |
LN_TR | 0.272504 | 0.000737 | / | 0.223213 |
LN_URB | 0.657178 | 0.580836 | 0.223213 | / |
Variable | At Level | At 1st Difference | At 2nd Difference | ||||||
---|---|---|---|---|---|---|---|---|---|
t-Statistic | Prob. | Stability | t-Statistic | Prob. | Stability | t-Statistic | Prob. | Stability | |
LN_EF | 471.754 | 0.000 | YES | 1544.46 | 0.000 | YES | 2387.88 | 0.000 | YES |
AG | 134.939 | 1.000 | NO | 209.403 | 0.999 | NO | 746.274 | 0.000 | YES |
LN_GDP | 275.842 | 0.559 | YES | 803.140 | 0.000 | YES | 1982.65 | 0.000 | YES |
LN_IND | 275.807 | 0.559 | NO | 1034.93 | 0.000 | YES | 2180.68 | 0.000 | YES |
LN_TR | 373.144 | 0.000 | YES | 1411.88 | 0.000 | YES | 2467.75 | 0.000 | YES |
LN_URB | 590.928 | 0.000 | YES | 875.738 | 0.000 | YES | 1101.56 | 0.000 | YES |
Threshold Variables | Threshold Number | F-Value | p-Value | Bootstrap Number | Critical Value | ||
---|---|---|---|---|---|---|---|
1% | 5% | 10% | |||||
AG | Single | 76.061 *** | 0.010 | 500 | 75.443 | 41.485 | 19.339 |
Double | 64.417 *** | 0.000 | 500 | 40.302 | 24.598 | 15.402 | |
Triple | 60.191 ** | 0.013 | 300 | 67.590 | 33.978 | 19.603 |
Variables | Fixed Effect Model | Threshold Model |
---|---|---|
AG | ||
LN_GDP | 0.0499 (0.106) | −0.9597 *** (q ≤ 1.871) (0.000) |
0.1859 *** (1.871 < q < 17.593) (0.000) | ||
0.1751 *** (q ≥ 17.593) (0.000) | ||
LN_IND | 0.2889 *** (0.000) | 0.2103 *** (0.000) |
LN_TR | 0.0227(0.101) | 0.0294 *** (0.030) |
LN_URB | 0.6241 *** (0.000) | 0.6109 *** (0.000) |
Constant | 7.3594 *** (0.000) | 8.1572 *** (0.000) |
R-squared within | 0.4566 | 0.4891 |
R-squared between | 0.6296 | 0.2082 |
R-squared overall | 0.6276 | 0.2097 |
F-test | 287.86 | 304.04 |
Prob > F | 0.0000 | 0.0000 |
Number of observations | 2240 | 2240 |
Number of groups | 140 | 140 |
Group | Threshold Variables | Threshold Number | F-Value | p-Value | Bootstrap Number | Critical Value | ||
---|---|---|---|---|---|---|---|---|
1% | 5% | 10% | ||||||
HI | AG | Single | 38.730 *** | 0.006 | 500 | 34.464 | 19.816 | 13.886 |
Double | 84.204 *** | 0.000 | 500 | 28.882 | 6.237 | −6.719 | ||
Triple | 40.707 *** | 0.000 | 300 | 29.892 | 19.560 | 14.136 | ||
UMI | AG | Single | 17.587 | 0.118 | 500 | 49.714 | 29.062 | 19.470 |
Double | 17.621 * | 0.082 | 500 | 64.367 | 25.424 | 14.482 | ||
Triple | 12.950 | 0.163 | 300 | 34.740 | 25.259 | 17.751 | ||
LMI | AG | Single | 115.386 *** | 0.000 | 500 | 100.894 | 35.747 | 25.009 |
Double | 37.176 ** | 0.018 | 500 | 41.387 | 25.704 | 18.641 | ||
Triple | 23.432 * | 0.070 | 300 | 45.436 | 31.898 | 20.665 | ||
LI | AG | Single | 44.610 *** | 0.000 | 500 | 38.670 | 21.882 | 15.278 |
Double | 44.828 *** | 0.010 | 500 | 45.100 | 25.714 | 18.335 | ||
Triple | 15.142 * | 0.097 | 300 | 33.401 | 21.971 | 15.105 |
Variable | Regression Coefficients and Significance Levels | |||
---|---|---|---|---|
HI | UMI | LMI | LI | |
LN_GDP | −1.0009 *** (q ≤ 2.937) (0.000) | 0.1032 (q ≤ 3.837) (0.205) | 0.4263 *** (q ≤ 2.432) (0.000) | 0.2887 *** (q ≤ 2.138) (0.000) |
3.3680 *** (2.937 < q < 3.021) (0.000) | 0.0810 (3.837 < q < 6.111) (0.319) | 0.3619 *** (2.432 < q < 4.388) (0.000) | 0.3347 *** (2.138 < q < 2.461) (0.000) | |
−0.1121 * (q ≥ 3.021) (0.099) | 0.0908 (q ≥ 6.111) (0.263) | 0.3457 *** (q ≥ 4.388) (0.000) | 0.3669 *** (q ≥ 2.461) (0.000) | |
LN_IND | 0.3999 *** (0.000) | 0.2815 *** (0.000) | 0.0658 ** (0.028) | 0.0647 *** (0.098) |
LN_TR | 0.0152 (0.584) | −0.0900 *** (0.002) | 0.1480 *** (0.000) | 0.0833 *** (0.000) |
LN_URB | −1.0608 *** (0.000) | 0.5114 *** (0.000) | 0.7806 *** (0.000) | 1.1213 *** (0.000) |
Constant | 12.7952 *** (0.000) | 7.9065 *** (0.000) | 9.1302 *** (0.000) | 8.5953 *** (0.000) |
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Li, S.; Li, R. Revisiting the Existence of EKC Hypothesis under Different Degrees of Population Aging: Empirical Analysis of Panel Data from 140 Countries. Int. J. Environ. Res. Public Health 2021, 18, 12753. https://doi.org/10.3390/ijerph182312753
Li S, Li R. Revisiting the Existence of EKC Hypothesis under Different Degrees of Population Aging: Empirical Analysis of Panel Data from 140 Countries. International Journal of Environmental Research and Public Health. 2021; 18(23):12753. https://doi.org/10.3390/ijerph182312753
Chicago/Turabian StyleLi, Shuyu, and Rongrong Li. 2021. "Revisiting the Existence of EKC Hypothesis under Different Degrees of Population Aging: Empirical Analysis of Panel Data from 140 Countries" International Journal of Environmental Research and Public Health 18, no. 23: 12753. https://doi.org/10.3390/ijerph182312753