Refugee Population and Environmental Quality in Sweden and Lebanon: Is Fertility Rate Changing the Dynamics?
Abstract
:1. Introduction
2. Literature: Theory and Empirical Highlights
3. Materials and Methods
3.1. Empirical Method
3.1.1. ARDL-Bound Test
3.1.2. Empirical Diagnostic Tests
4. Discussion of Findings
4.1. Greenhouse Gas Emissions and Income
4.2. Greenhouse Gas Emissions and Fertility
4.3. Greenhouse Gas Emissions and Refugees
4.4. Greenhouse Gas Emissions and Natural Resource Rent
4.5. Robustness Evidence
5. Conclusions and Policy Recommendation
Policy Recommendation
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
Appendix A
1 | According to the World Bank, the 1951 Convention Relating to the Status of its 1967 Protocol, and the 1969 Organization of African Unity Convention Governing the Specific Aspects of Refugee Problems in Africa, people recognized as refugees in accordance with the UNHCR statute, people granted refugee-like humanitarian status, and people provided temporary protection. Asylum seekers—people who have applied for asylum or refugee status and who have not yet received a decision, or who are registered as asylum seekers—are excluded. Palestinian refugees are people (and their descendants) whose residence was Palestine between June 1946 and May 1948, and who lost their homes and means of livelihood because of the 1948 Arab–Israeli conflict. |
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Study | Period | Country | Key Variables | Method | Findings |
---|---|---|---|---|---|
Bildirici (2022) | From 1996 to 2019 | Bangladesh, Ethiopia, Jordan, Lebanon, Pakistan, Sudan, and Uganda | Greenhouse gas (GHG) emissions, deforestation, and refugee populations | Panel quantile autoregressive distributed lag (PQARDL) | Refugee population causes a decline in forest area and an increase in greenhouse gas emissions. |
Aydin and Bozatli (2023) | From 1979 to 2019 | Turkey | Effects of refugee population, renewable energy consumption, carbon emissions, and economic growth on health expenditure | Fourier analysis | Air pollution increases health expenditures. Refugee population is an important determinant of health expenditures. |
Subramaniam et al. (2023) | From 1980 to 2019 | 29 developing countries | CO2 emissions, environmental degradation, the refugee population | Fully modified ordinary least squares (FMOLS); dynamic ordinary least-squares estimator (DOLS) | Migration worsens environmental degradation. Environmental degradation appears to be greater in countries with higher immigration. |
Bu et al. (2022) | 2017 | China | Population migration affects carbon emissions | GWR (geographically weighted regression); EE MRIO (environmentally extended multi-regional input–output) | Population migration exacerbates carbon emission inequality, especially increasing provinces’ carbon emission reduction barrier with net out-migrants. |
Squalli (2021) | From 1997 to 2014 | USA | Migration and environmental emissions. | NAICS (North American Industry Classification System); SIC (Standard Industrial Classification) | Overall population contributes to environmental emissions at the US state level. Immigration may indeed yield environmental benefits. Second, US states with lower GHG emissions are associated with a higher share of immigrants. |
Study | Period | Countries | Key Variables | Method | Findings |
---|---|---|---|---|---|
Kim et al. (2020) | From 1998 to 2016 | 16 South Korean provinces | Income, CO2 emissions, and population aging | (FMOLS) | Population aging reduces carbon emissions and younger populations spur carbon emissions. |
Alola et al. (2019a) | From 1997 to 2014 | 16 EU countries | Economic growth, fertility rate, energy indicators, and ecological footprint | Panel pool mean group autoregressive distributive lag (PMG-ARDL) | Fertility and renewable energy consumption mitigates ecological footprint, thereby promoting environmental sustainability. |
Alola et al. (2020) | From 1990 to 2014 | USA and Canada | Biocapacity, fertility, marriage, and ICT | Autoregressive distributive lag (ARDL) | Marriage mitigates (promotes) biocapacity in Canada (USA). Fertility moderate marriage to cause a negative impact on biocapacity in the USA. |
Zhang and Tan (2016) | From 1997 to 2016 | 29 provinces in China | Population factors and carbon emissions. | Stochastic impacts by regression on population, affluence, and technology (STIRPAT) | Population aging and population quality positively correlate with carbon emissions. Population living standard exhibits urban–rural difference in carbon emissions. Population aging–carbon emissions nexus varies across regions. |
Brauner-Otto (2014) | From 1997 to 2013 | Chitwan, Nepal | Environmental quality and fertility. | Shannon–Weiner diversity index | The result reveals that women living in poor environmental conditions are likely to have larger families. |
Simon (2017) | From 1971 to 2010 | Rural areas of Mexico | Precipitation and fertility | Vicious circle model (VCM) | The nexus of fertility and environmental quality depends on the climatic condition in Mexico. |
Variable | Symbol | Measurement | Source |
---|---|---|---|
Greenhouse gas per capita | GHGC | kt of CO2 equivalent | Estimated from WDI |
Gross domestic product per capita | Y | Constant 2015 USD | WDI |
Natural resource rent | N | Total natural resource rents (% of GDP) | WDI |
Fertility | F | Total births per woman | WDI |
Refugee | R | as defined under the 1951 Convention Relating to the status of its 1967 Protocol1. | WDI |
Sweden | |||||||
---|---|---|---|---|---|---|---|
lGHGC | lY | lY2 | lN | lF | lR | lR*lF | |
Mean | −5.022 | 10.684 | 114.181 | −0.741 | 0.580 | 11.839 | 6.855 |
Median | −4.964 | 10.736 | 115.278 | −0.914 | 0.595 | 11.879 | 7.054 |
Maximum | −4.720 | 10.901 | 118.844 | 0.510 | 0.756 | 12.444 | 8.774 |
Minimum | −5.418 | 10.388 | 107.914 | −1.444 | 0.405 | 11.203 | 4.857 |
Std. Dev. | 0.223 | 0.168 | 3.591 | 0.531 | 0.098 | 0.418 | 1.088 |
Skewness | −0.466 | −0.457 | −0.444 | 0.848 | −0.173 | −0.133 | −0.165 |
Kurtosis | 1.798 | 1.740 | 1.731 | 2.551 | 2.228 | 1.701 | 2.378 |
Jarque–Bera | 3.088 | 3.228 | 3.199 | 4.110 | 0.955 | 2.343 | 0.661 |
Lebanon | |||||||
Mean | −5.413 | 8.790 | 77.339 | −5.828 | 0.870 | 13.243 | 11.478 |
Median | −5.372 | 8.790 | 77.270 | −6.264 | 0.785 | 12.940 | 11.052 |
Maximum | −5.067 | 9.109 | 82.975 | −3.323 | 1.192 | 14.289 | 15.073 |
Minimum | −6.203 | 7.920 | 62.738 | −6.852 | 0.732 | 12.641 | 9.575 |
Std. Dev. | 0.273 | 0.258 | 4.459 | 0.949 | 0.139 | 0.603 | 1.531 |
Skewness | −1.382 | −1.265 | −1.148 | 1.131 | 0.911 | 0.849 | 0.857 |
Kurtosis | 4.574 | 5.291 | 4.875 | 3.039 | 2.536 | 1.909 | 2.764 |
Jarque–Bera | 13.502 | 15.538 | 11.721 | 6.830 | 4.718 | 5.438 | 3.995 |
Sweden | |||||||
lGHGC | lY | lY2 | lN | lF | lR | lR*lF | |
lGHGC | 1 | ||||||
LY | −0.889 | 1 | |||||
lY2 | −0.891 | 0.999 | 1 | ||||
lN | −0.551 | 0.564 | 0.565 | 1 | |||
lF | −0.087 | −0.034 | −0.032 | 0.362 | 1 | ||
lR | −0.269 | −0.035 | −0.031 | −0.125 | −0.425 | 1 | |
lR*lF | −0.152 | −0.045 | −0.042 | 0.357 | 0.978 | −0.230 | 1 |
Lebanon | |||||||
lGHGC | lY | lY2 | lN | lF | lR | lR*lF | |
lGHGC | 1 | ||||||
LY | 0.846 | 1 | |||||
lY2 | 0.842 | 0.999 | 1 | ||||
lN | −0.784 | −0.803 | −0.800 | 1 | |||
lF | −0.867 | −0.882 | −0.882 | 0.869 | 1 | ||
lR | 0.651 | 0.549 | 0.553 | −0.447 | −0.624 | 1 | |
lR*lF | −0.807 | −0.854 | −0.854 | 0.870 | 0.971 | −0.423 | 1 |
Sweden | ||||||||
ADF | PP | |||||||
Variables | I(0) | I(0) with Suppressed Constant | I(1) | I(1) with Suppressed Constant | I(0) | I(0) with Suppressed Constant | I(1) | I(1) with Suppressed Constant |
t-stat | t-stat | t-stat | t-stat | t-stat | t-stat | t-stat | p-value | |
lGHGC | 0.801 | 2.728 | −6.568 a | −5.221 a | 1.264 | 3.150 | −6.548 a | −5.324 a |
lY | −0.573 | 3.187 | −4.473 a | −3.396 a | −0.588 | 2.973 | −4.391 a | −3.368 a |
lY2 | −0.553 | 3.175 | −4.511 a | −3.421 a | −0.566 | 2.986 | −4.431 a | −3.396 a |
lN | −2.315 | −1.259 | −6.460 a | −6.574 a | −2.226 | −0.996 | −6.993 a | −7.132 a |
lF | −1.697 | −1.610 c | −2.122 | −2.106 b | −1.985 | −1.119 | −2.187 | −2.155 b |
lR | −0.514 | 0.990 | −2.858 c | −2.896 a | −1.146 | 0.632 | −2.818 c | −2.850 a |
lR*lF | −1.761 | −1.427 | −2.392 | −2.369 b | −2.007 | −1.031 | −2.464 | −2.428 b |
Lebanon | ||||||||
ADF | PP | |||||||
Variables | I(0) | I(0) with Suppressed Constant | I(1) | I(1) with Suppressed Constant | I(0) | I(0) with Suppressed Constant | I(1) | I(1) with Suppressed Constant |
t-stat | t-stat | t-stat | t-stat | t-stat | t-stat | t-stat | t-stat | |
lGHGC | −3.405 b | −2.579 b | −5.538 a | −4.973 a | −3.629 b | −2.357 b | −5.530 a | −4.986 a |
lY | −5.438 a | 1.426 | −5.362 a | −5.673 a | −4.430 a | 1.039 | −5.213 a | −5.496 a |
lY2 | −5.105 a | 1.293 | −4.984 a | −5.273 a | −4.196 a | 0.918 | −4.885 a | −5.150 a |
lN | −2.962 b | 0.581 | −6.341 a | −6.279 a | −3.084b | 0.650 | −6.364 a | −6.275 a |
lF | −6.040 a | −6.484 a | −1.477 | −1.613 c | −3.944 a | −3.738 a | −1.534 | −1.623 c |
lR | −0.346 | 1.650 | −3.317 b | −3.192 a | −0.572 | 1.367 | −3.214 b | −3.115 a |
lR*lF | −4.167 a | −3.903 a | −1.773 | −1.760 c | −2.891 c | −2.303 b | −1.795 | −1.754 c |
Model 1 (Fertility) | Model 2 (Refugee) | Model 3 (Refugee*Fertility) | ||||
---|---|---|---|---|---|---|
Indicators | Coefficient | p-Value | Coefficient | p-Value | Coefficient | p-Value |
lY | 27.346 b | 0.035 | 0.920 b | 0.015 | 0.230 a | 0.008 |
lY2 | −1.270 b | 0.037 | −0.030 a | 0.002 | −0.036 b | 0.017 |
lN | 0.005 | 0.790 | 0.003 | 0.843 | 0.014 | 0.475 |
lN−1 | −0.031 c | −0.071 | −0.030 c | 0.086 | ||
lF | 0.178 b | 0.045 | 2.980 b | 0.023 | ||
lR | −0.048 b | 0.046 | ||||
lR*lF | −0.211 b | 0.043 | ||||
Bound Test | 5.675 a | 6.045 a | 5.493 a | |||
Het Test | 0.350 | 0.710 | 0.862 | |||
SR | 0.050 | 0.100 | 0.121 | |||
Stability | Stable | Stable | Stable |
Model 1 (Fertility) | Model 2 (Refugee) | Model 3 (Refugee*Fertility) | ||||
---|---|---|---|---|---|---|
Indicators | Coefficient | p-Value | Coefficient | p-Value | Coefficient | p-Value |
lY | −121.639 a | 0.009 | −0.522 b | 0.026 | −0.526 b | 0.037 |
lY2 | 6.870 a | 0.009 | 0.039 b | 0.028 | 0.039 c | 0.050 |
lN | 0.105 b | 0.042 | 0.020 | 0.437 | −0.019 | 0.521 |
lN−1 | 0.062 | 0.208 | −0.030 c | 0.086 | ||
lF | 0.315 | 0.894 | 2.980 b | 0.023 | ||
lF−2 | 6.222 c | 0.075 | ||||
lR | −0.130 | 0.139 | ||||
lR−1 | 0.163 c | 0.065 | ||||
lR*lF | 0.001 | 0.961 | ||||
Bound Test | 2.800 c | 5.429 a | 4.334 a | |||
Het Test | 1.000 | 0.124 | 0.862 | |||
SR | 0.401 | 0.070 | 0.074 | |||
Stability | Stable | Stable | Stable |
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Alola, A.A.; Aliyev, A.; Obekpa, H.O.; Olagunju, I. Refugee Population and Environmental Quality in Sweden and Lebanon: Is Fertility Rate Changing the Dynamics? Soc. Sci. 2023, 12, 243. https://doi.org/10.3390/socsci12040243
Alola AA, Aliyev A, Obekpa HO, Olagunju I. Refugee Population and Environmental Quality in Sweden and Lebanon: Is Fertility Rate Changing the Dynamics? Social Sciences. 2023; 12(4):243. https://doi.org/10.3390/socsci12040243
Chicago/Turabian StyleAlola, Andrew Adewale, Anar Aliyev, Hephzibah Onyeje Obekpa, and Ishaaqa Olagunju. 2023. "Refugee Population and Environmental Quality in Sweden and Lebanon: Is Fertility Rate Changing the Dynamics?" Social Sciences 12, no. 4: 243. https://doi.org/10.3390/socsci12040243
APA StyleAlola, A. A., Aliyev, A., Obekpa, H. O., & Olagunju, I. (2023). Refugee Population and Environmental Quality in Sweden and Lebanon: Is Fertility Rate Changing the Dynamics? Social Sciences, 12(4), 243. https://doi.org/10.3390/socsci12040243