The Nexus Between Tourism and Environmental Quality in Countries Most Dependent on Tourism: A RALS Approach to the Cointegration Test
Abstract
1. Introduction
The Conceptual Framework
2. Literature Review
2.1. Tourism vs. Carbon Emissions
2.2. EI and Carbon Emissions
2.3. EG and Carbon Emissions
2.4. Population, Life Expectancy, and Carbon Emissions
3. Methodology
3.1. Data
3.2. Econometric Structure
4. Results and Discussion
4.1. Unit Root and Cointegration Estimations
4.2. ARDL Short- and Long-Term Estimations
5. Conclusions
5.1. Policy Recommendations
5.2. Limitations
5.3. Future Studies
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Sr. No. | Authors | Time Period | Country | Method | Results Remarks |
1 | [5] | 1995–2018 | 10 GDPC | MMQR | TOUR (+), sig → CO2 EG (+), sig → CO2 |
2 | [4] | 1990–2019 | Brazil | ARDL | TOUR (+), sig → CO2 EG (+), sig → CO2 |
3 | [34] | 1997–2012 | China | EEBT and SDA | TOUR (+), sig → CO2 |
4 | [10] | 1990–2020 | Thailand | ARDL | TOUR (+), sig → CO2 EG (+), sig → CO2 |
5 | [9] | 1995-2020 | Kuwait | VEC | TOUR (+), sig → CO2 EG (+), sig → CO2 |
6 | [8] | 1995–2018 | MVC | FEPQR | TOUR (+), sig → CO2 |
7 | [43] | 2001–2017 | 15 MC | Threshold OLS | TOUR (−), sig → CO2 |
8 | [44] | 1994–2014 | Thailand | Bootstrapping ARDL | TOUR (−), sig → CO2 |
9 | [35] | 2000–2017 | 70 countries | GNS | TOURDEV (+), sig → CO2 (DE) TOURDEV (−), sig → CO2 (IE) |
10 | [6] | 1995–2014 | TIT | BPGC | TOUR (+), sig → CO2 EG (+), sig → CO2 |
11 | [42] | 1998–2014 | 95 countries | PCSE | TOUR (−), sig → CO2 EG (−), sig → CO2 EG2 (+), sig → CO2 |
12 | [41] | 2001–2017 | USA | PMWC | TOUR (+), sig → CO2 |
13 | [46] | 1970–2019 | Malaysia | QARDL | TOURDEV (−), sig → CO2 |
14 | [37] | 1980–2018 | South Asia | NARDL | TOUR (+), sig → CO2 |
15 | [39] | 2005–2016 | China | SEM | TOUR (+, −), sig → CO2 (IUSC UDTA) TOUR (−, +), sig → CO2 (USC-DTA) |
16 | [36] | 2000–2020 | DDC | GMM, FMOLS, and DOLS | TOUR (+), sig → CO2 |
17 | [40] | 1995–2014 | 12 PCC | GNC | TOUR (+), sig → CO2 |
18 | [38] | 1990–2020 | Turkey | DOLS | TOUR (+), sig → CO2 EG (+), sig → CO2 |
19 | [45] | 1995Q1–2017Q4 | China | QARDL | TOURDEV (−), sig → CO2 (LT) EG (+), sig → CO2 (LT) |
20 | [47] | 1990–2014 | DC | FE-PQR | EI (−), sig → CO2 EG(Y) (+), sig → CO2 EG2 (Y2) (−), sig → CO2 |
21 | [49] | 1990–2018 | 25 LEE | ARDL | EI (+), sig → CI |
22 | [48] | 2005–2018 | BRICS and OECD | 2SLS and GMM | EI (−), sig → CI |
23 | [51] | 1980–2018 | 50 African countries | PCS-DL | EI (+), sig → CI EG (+), sig → CO2 |
24 | [52] | 1995–2017 | DTE | MG and AMG | EI (+), sig → CO2 EG (+), sig → CO2 EG2 (−), sig → CO2 POP (+), sig → CO2 |
25 | [53] | 1990–2020 | Morocco | ARDL | EI (+), sig →CO2 |
26 | [54] | 1980–2019 | China | ARDL | GDP (+), sig → CO2 GDP2 (−), sig → CO2 EN (+), sig → CO2 |
27 | [50] | 2006–2019 | China | SER | EI (−), sig → CO2 |
28 | [55] | 1990–2017 | HULG | GMM | EI (+), sig → CO2 (HIG) |
29 | [83] | 1990–2018 | India | VECM | EG (+), sig → CO2 |
30 | [84] | 2001–2016 | China | Spatial econometric model | EI (+), sig → CO2 EG (+), sig → CO2 |
31 | [85] | 1980–2019 | African economies | PLS | EG (+), sig → CO2 |
32 | [86] | 2000–2014 | 35 OECD | GMM and PVAR | EI (+), sig → EP EG (+), sig → EP |
33 | [88] | 1987–2019 | France | ARDL | EG (+), sig → CO2 |
34 | [57] | 1990–2014 | SADE | FMOLS and DOLS | EG (+), sig → CO2 EG2 (−), sig → CO2 |
35 | [90] | 1990–2018 | SEC | CCEMG | EG (+), sig → CO2 EP (−), sig → CO2 |
36 | [60] | 1990–2018 | LMHIAC | MG | EG (+), sig → CO2 EG (−), sig → CO2 |
37 | [89] | 1990–2019 | BRICS | FE | EG (+), sig → CO2 URB (+), sig → CO2 |
38 | [87] | 2007–2015 | OECD | Regression | EG (+), sig → CO2 |
39 | [56] | 1986–2018 | Vietnam | Threshold regression | EG (+), sig → CO2 EG2 (−), sig → CO2 FE (+), sig → CO2 |
40 | [58] | 1990–2017 | WAME | CUP-FM and CUP-BC | EG (+), sig → EF EG2 (−), sig → EF POP (+), sig → EF |
41 | [12] | 1971–2016 | USMCA | AMG | EG (−), sig → CO2 Canada and USA EG (+), sig → CO2 Mexico EG2 (+), sig → CO2 Canada and USA EG2 (−), sig → CO2 Mexico |
42 | [80] | 1995–2020 | SAC | PMG, MG, and DFE | EG (+), sig → CO2 |
43 | [69] | 1971–2019 | Turkey | AARDL and DARDL | LE (+), sig → EF EG (+), sig → EF EG2 (−), sig → EF |
44 | [70] | 1992–2016 | 154 countries | Threshold regression | URB (+), sig → CO2 LE (−), sig → CO2 POP (+), sig → CO2 EG (+), sig → CO2 EG2 (−), sig → CO2 |
45 | [67] | 1975–2020 | Pakistan | ARDL | CO2 (−), sig → LE |
46 | [71] | 1999–2016 | BRICS | PARDL, PCSE, FGLS, and PMG | LE (+), sig → EQ (LT) EG (+), sig → EQ (LT) LE (−), sig → EQ (ST) EG (−), sig → EQ (ST) |
47 | [17] | 1960–2018 | Turkey | WC, FTY, and BCFDSC | LE (+), sig → CO2 |
48 | [68] | 1971–2016 | USA | GMM and GLM | PG (+), sig → CO2 and EF |
49 | [72] | 1990–2017 | Next 11 | BQR | POP (+), sig → AP |
50 | [74] | 2000–2015 | 46 SSA | GMM | POP (+), sig → CO2 |
51 | [73] | 1990–2019 | PIIGS | DOLS | URBPOP (+), sig → CO2 |
52 | [76] | 2000–2015 | BTHUA | Entropy method | URBPOP (+), sig → EQ URBPOP (−), sig → EQ |
Note: MMQR = method of moment quantile regression; ARDL = autoregressive distributed lag model; EEBT = emissions embodied in the bilateral trade; SDA = structure decomposition analysis; TOUR = tourism; EG = economic growth; CO2 = carbon emissions; VEC = vector error correction model; FMOLS = fully modified ordinary least squares; FEPQR = fixed-effect panel quantile regression; OLS = ordinary least squares; GNS = generalized nested spatial model; BPGC = bootstrap panel Granger causality; PCSE = panel-corrected standard error model; PMWC = partial and multiple wavelet coherence; USA = United States of America; SBM-DEA = super-efficiency slacks-based measure and data envelope analyses; QARDL = quantile autoregressive distributed lag model; PARDL = panel autoregressive distributed lag model; SEM = spatial error model; GMM = generalized moment method; DOLS = dynamic ordinary least squares; GNC = Granger non-causality test; FE-PQR = fixed-effect panel quantile regression; 2SLS = two-stage least squares; PCS-DL = panel cross-sectional augmented distributed lags; MG = mean group; AMG = augmented mean group; SER = spatial econometric regression; VECM = vector error correction model; PLS = panel least squares; FE-2SLS = fixed-effect two-stage least squares; PVAR = panel vector autoregressive regression; CCEMG = common correlated effects mean group; FE = fixed-effect estimation model; CUP-FM = continuously updated fully modified; CUP-BC = continuously updated bias-corrected; DFE = dynamic fixed effect; AARDL = augmented autoregressive distributed lag model; DARDL = dynamic augmented autoregressive distributed method; PCSE = panel-correlated standard error; FGLS = feasible generalized least squares; WC = wavelet coherence; FTY = Fourier Toda–Yamamoto; BCFDSC = Breitung–Candelon frequency-domain spectral causality; GLM = generalized linear model; BQR = bootstrap quantile regression; BTHUA = Beijing–Tianjin–Hebei urban agglomeration; PIIGS = Portugal, Ireland, Italy, Greece, and Spain; SSA = Sub-Saharan African countries; BRICS = Brazil, Russia, India, China, and South Africa; SAC = South American countries; USMCA = USA–Mexico–Canada Agreement; WAME = West Asian and Middle East nations; OECD = Organization of Economic Coordination and Development; LMHIAC = low-income, middle-income, and high-income Asian countries; SEC = South European countries; SADE = South Asian developing economies; HULG = higher-, upper-middle-, lower-middle-, and low-income groups; DTE = developing and transition economies; LEE = large emerging economies; DC = developing countries; PCC = post-communist countries; DDC = developing and developed countries; TIT = tourism island territories; MC = Mediterranean countries; MVC = most visited countries; GDPC = gross domestic product countries; DE = direct effect; IE = indirect effect; ST = short term; LT-IUSC = long-term inverted U-shaped curve; LQ = lower quantile; UQ-IUSC = upper quantile inverted U-shaped curve; IUSC UDTA = inverted U-shaped in underdeveloped tourism areas; USC-DTA = U-shaped in developed tourism areas; CI = carbon intensity; HIG = high-income group; EP = environmental performance; AP = air pollution; TOURDEV = tourism development; EG2 = economic growth squared; EI = energy intensity; GDP = gross domestic product; GDP2 = gross domestic product squared; POP = population; EN = energy; EP = energy productivity; URB = urbanization; FE = fossil energy; LE = life expectancy; PG = population growth; URBPOP = urban population. |
References
- Rezapouraghdam, H.; Alipour, H.; Arasli, H. Workplace spirituality and organization sustainability: A theoretical perspective on hospitality employees’ sustainable behavior. Environ. Dev. Sustain. 2019, 21, 1583–1601. [Google Scholar] [CrossRef]
- WTO. Tourism in the 2030 Agenda. 2023. Available online: https://www.unwto.org/tourism-in-2030-agenda (accessed on 21 October 2023).
- WTTC. Economic Impact Research. 2023. Available online: https://assets-global.website-files.com/6329bc97af73223b575983ac/647df24b7c4bf560880560f9_EIR2023-APEC.pdf (accessed on 21 October 2023).
- Raihan, A. Economic growth and carbon emission nexus: The function of tourism in Brazil. J. Econ. Stat. 2023, 1, 68–80. [Google Scholar] [CrossRef]
- Razzaq, A.; Fatima, T.; Murshed, M.M. Asymmetric effects of tourism development and green innovation on economic growth and carbon emissions in Top 10 GDP Countries. J. Environ. Plan. Manag. 2023, 66, 471–500. [Google Scholar] [CrossRef]
- Akadiri, S.S.; Lasisi, T.T.; Uzuner, G.; Akadiri, A.C. Examining the causal impacts of tourism, globalization, economic growth and carbon emissions in tourism island territories: Bootstrap panel Granger causality analysis. Curr. Issues Tour. 2020, 23, 470–484. [Google Scholar] [CrossRef]
- Skendžić, S.; Zovko, M.; Živković, I.P.; Lešić, V.; Lemić, D. The impact of climate change on agricultural insect pests. Insects 2021, 12, 440. [Google Scholar] [CrossRef]
- Erdoğan, S.; Gedikli, A.; Cevik, E.I.; Erdoğan, F. Eco-friendly technologies, international tourism and carbon emissions: Evidence from the most visited countries. Technol. Forecast. Soc. Change 2022, 180, 121705. [Google Scholar] [CrossRef]
- Khan, A.M.; Khan, U.; Naseem, S.; Faisal, S. Role of energy consumption, tourism and economic growth in carbon emission: Evidence from Kuwait. Cogent Econ. Financ. 2023, 11, 2218680. [Google Scholar] [CrossRef]
- Raihan, A.; Muhtasim, D.A.; Farhana, S.; Rahman, M.; Hasan, M.A.U.; Paul, A.; Faruk, O. Dynamic linkages between environmental factors and carbon emissions in Thailand. Environ. Process. 2023, 10, 5. [Google Scholar] [CrossRef]
- WTO; ITF. Transport-Related CO2 Emissions of the Tourism Sector–Modelling Results; UNWTO: Madrid, Spain, 2019. [Google Scholar] [CrossRef]
- Ongan, S.; Işık, C.; Amin, A.; Bulut, U.; Rehman, A.; Alvarado, R.; Ahmad, M.; Karakaya, S. Are economic growth and environmental pollution a dilemma? Environ. Sci. Pollut. Res. 2023, 30, 49591–49604. [Google Scholar] [CrossRef]
- Meșter, I.; Simuț, R.; Meșter, L.; Bâc, D. An investigation of tourism, economic growth, CO2 emissions, trade openness and energy intensity index nexus: Evidence for the European Union. Energies 2023, 16, 4308. [Google Scholar] [CrossRef]
- Khanal, A.; Rahman, M.M.; Khanam, R.; Velayutham, E. Are tourism and energy consumption linked? Evidence from Australia. Sustainability 2021, 13, 10800. [Google Scholar] [CrossRef]
- Katircioglu, S.T.; Feridun, M.; Kilinc, C. Estimating tourism-induced energy consumption and CO2 emissions: The case of Cyprus. Renew. Sustain. Energy Rev. 2014, 29, 634–640. [Google Scholar] [CrossRef]
- Rahman, M.M. Do population density, economic growth, energy use and exports adversely affect environmental quality in Asian populous countries? Renew. Sustain. Energy Rev. 2017, 77, 506–514. [Google Scholar] [CrossRef]
- Rjoub, H.; Odugbesan, J.A.; Adebayo, T.S.; Wong, W.-K. Investigating the causal relationships among carbon emissions, economic growth, and life expectancy in Turkey: Evidence from time and frequency domain causality techniques. Sustainability 2021, 13, 2924. [Google Scholar] [CrossRef]
- Al Fahmawee, E.A.D.; Jawabreh, O. Sustainability of green tourism by international tourists and its impact on green environmental achievement: Petra heritage, Jordan. Geo J. Tour. Geosites 2023, 46, 27–36. [Google Scholar] [CrossRef]
- Cho, J.S.; Greenwood-Nimmo, M.; Shin, Y. Recent developments of the autoregressive distributed lag modelling framework. J. Econ. Surv. 2023, 37, 7–32. [Google Scholar] [CrossRef]
- Befikadu, A.T. An Empirical Analysis of the Effects of Population Growth on Economic Growth in Ethiopia Using an Autoregressive Distributive Lag (ARDL) Model Approach. J. Knowl. Econ. 2023, 15, 8209–8230. [Google Scholar] [CrossRef]
- Liao, H.; Chen, Y.; Tan, R.; Chen, Y.; Wei, X.; Yang, H. Can natural resource rent, technological innovation, renewable energy, and financial development ease China’s environmental pollution burden? New evidence from the nonlinear-autoregressive distributive lag model. Resour. Policy 2023, 84, 103760. [Google Scholar] [CrossRef]
- García, D.H.; Rezapouraghdam, H. Climate change, heat stress and the analysis of its space-time variability in European metropolises. J. Clean. Prod. 2023, 425, 138892. [Google Scholar] [CrossRef]
- Hidalgo-García, D.; Founda, D.; Rezapouraghdam, H. Spatiotemporal variability of the Universal Thermal Climate Index during heat waves using the UrbClim climate model: Implications for tourism destinations. Urban Clim. 2025, 59, 102281. [Google Scholar] [CrossRef]
- García, D.H.; Rezapouraghdam, H.; Hall, C.M.; Karatepe, O.M.; Koupaei, S.N. Spatio-temporal variability of the earth’s surface temperature and the changes in land user/land cover: Implications for sustainable tourism development. J. Policy Res. Tour. Leis. Events 2023, 17, 557–584. [Google Scholar] [CrossRef]
- Rezapouraghdam, H.; Karatepe, O.M.; Enea, C. Sustainable recovery for people and the planet through spirituality-induced connectedness in the hospitality and tourism industry. J. Hosp. Tour. Insights 2023, 6, 1776–1795. [Google Scholar] [CrossRef]
- Alipour, H.; Rezapouraghdam, H.; Akhshik, A. Heritage redemption and the curse of tourism: The case of world’s last inhabited troglodyte village. Tour. Plan. Dev. 2021, 18, 68–85. [Google Scholar] [CrossRef]
- Rezapouraghdam, H.; Hidalgo-García, D. Urban Development and Climate Change: Implications for Educational Tourism Destination Planning. Water Air Soil Pollut. 2024, 235, 319. [Google Scholar] [CrossRef]
- Holden, E.; Linnerud, K.; Banister, D. Sustainable development: Our common future revisited. Glob. Environ. Change 2014, 26, 130–139. [Google Scholar] [CrossRef]
- Rezapouraghdam, H.; Alipour, H.; Akhshik, A. A futuristic approach to sustainable tourism development: Lessons from Kandovan Village. In Strategies for Promoting Sustainable Hospitality and Tourism Services; IGI Global: Hershey, PA, USA, 2020; pp. 140–157. [Google Scholar] [CrossRef]
- Rezapouraghdam, H.; Akhshik, A. Tracing the complexity-sustainability nexus in a small Mediterranean island: Implications for hospitality and tourism education. Worldw. Hosp. Tour. Themes 2021, 13, 476–487. [Google Scholar] [CrossRef]
- Puga-Bonilla, M.; Hidalgo-García, D.; Rezapouraghdam, H.; Bolivar, F.J.L. Risk of mortality and disease attributable to the heat stress index and its variability during heat waves: An observational study on the city of Madrid. Sustain. Cities Soc. 2025, 121, 106189. [Google Scholar] [CrossRef]
- Agbanike, T.F.; Nwani, C.; Uwazie, U.I.; Uma, K.E.; Anochiwa, L.I.; Igberi, C.O.; Enyoghasim, M.O.; Uwajumogu, N.R.; Onwuka, K.O.; Ogbonnaya, I.O. Oil, environmental pollution and life expectancy in Nigeria. Appl. Ecol. Environ. Res. 2019, 17, 11143–11162. [Google Scholar] [CrossRef]
- Wiredu, J.; Yang, Q.; Lu, T.; Sampene, A.K.; Wiredu, L.O. Delving into environmental pollution mitigation: Does green finance, economic development, renewable energy resource, life expectancy, and urbanization matter? Environ. Dev. Sustain. 2025, 17, 1–30. [Google Scholar] [CrossRef]
- Dong, S.; Xia, B.; Li, F.; Cheng, H.; Li, Z.; Li, Y.; Zhang, W.; Yang, Y.; Liu, Q.; Li, S. Spatial–temporal pattern, driving mechanism and optimization policies for embodied carbon emissions transfers in multi-regional tourism: Case study of provinces in China. J. Clean. Prod. 2023, 382, 135362. [Google Scholar] [CrossRef]
- Liu, Z.; Lan, J.; Chien, F.; Sadiq, M.; Nawaz, M.A. Role of tourism development in environmental degradation: A step towards emission reduction. J. Environ. Manag. 2022, 303, 114078. [Google Scholar] [CrossRef] [PubMed]
- Khan, Y.A.; Ahmad, M. Investigating the impact of renewable energy, international trade, tourism, and foreign direct investment on carbon emission in developing as well as developed countries. Environ. Sci. Pollut. Res. Int. 2021, 28, 31246–31255. [Google Scholar] [CrossRef] [PubMed]
- Chishti, M.Z.; Ullah, S.; Ozturk, I.; Usman, A. Examining the asymmetric effects of globalization and tourism on pollution emissions in South Asia. Environ. Sci. Pollut. Res. Int. 2020, 27, 27721–27737. [Google Scholar] [CrossRef]
- Raihan, A.; Tuspekova, A. Dynamic impacts of economic growth, renewable energy use, urbanization, industrialization, tourism, agriculture, and forests on carbon emissions in Turkey. Carbon Res. 2022, 1, 20. [Google Scholar] [CrossRef]
- Huang, C.; Wang, J.-W.; Wang, C.-M.; Cheng, J.-H.; Dai, J. Does tourism industry agglomeration reduce carbon emissions? Environ. Sci. Pollut. Res. 2021, 28, 30278–30293. [Google Scholar] [CrossRef] [PubMed]
- Isaeva, A.; Salahodjaev, R.; Khachaturov, A.; Tosheva, S. The impact of tourism and financial development on energy consumption and carbon dioxide emission: Evidence from post-communist countries. J. Knowl. Econ. 2022, 13, 773–786. [Google Scholar] [CrossRef]
- Mishra, S.; Sinha, A.; Sharif, A.; Suki, N.M. Dynamic linkages between tourism, transportation, growth and carbon emission in the USA: Evidence from partial and multiple wavelet coherence. Curr. Issues Tour. 2020, 23, 2733–2755. [Google Scholar] [CrossRef]
- Le, T.-H.; Nguyen, C.P. The impact of tourism on carbon dioxide emissions: Insights from 95 countries. Appl. Econ. 2021, 53, 235–261. [Google Scholar] [CrossRef]
- Yıldırım, S.; Yıldırım, D.Ç.; Aydın, K.; Erdoğan, F. Regime-dependent effect of tourism on carbon emissions in the Mediterranean countries. Environ. Sci. Pollut. Res. 2021, 28, 54766–54780. [Google Scholar] [CrossRef]
- Yue, X.-G.; Liao, Y.; Zheng, S.; Shao, X.; Gao, J. The role of green innovation and tourism towards carbon neutrality in Thailand: Evidence from bootstrap ADRL approach. J. Environ. Manag. 2021, 292, 112778. [Google Scholar] [CrossRef]
- Razzaq, A.; Sharif, A.; Ahmad, P.; Jermsittiparsert, K. Asymmetric role of tourism development and technology innovation on carbon dioxide emission reduction in the Chinese economy: Fresh insights from QARDL approach. Sustain. Dev. 2021, 29, 176–193. [Google Scholar] [CrossRef]
- Sun, Y.; Kamran, H.W.; Razzaq, A.; Qadri, F.S.; Suksatan, W. Dynamic and causality linkages from transportation services and tourism development to economic growth and carbon emissions: New insights from Quantile ARDL approach. Integr. Environ. Assess. Manag. 2022, 18, 1272–1287. [Google Scholar] [CrossRef] [PubMed]
- Akram, R.; Chen, F.; Khalid, F.; Ye, Z.; Majeed, M.T. Heterogeneous effects of energy efficiency and renewable energy on carbon emissions: Evidence from developing countries. J. Clean. Prod. 2020, 247, 119122. [Google Scholar] [CrossRef]
- Xiaoyang, X.; Kanaado, M.B.; Epadile, M. The impact of technological innovation, research and development, and energy intensity on carbon emissions: An experience from BRICS and OECD countries. Int. J. Sustain. Dev. World Policy 2022, 11, 1–17. [Google Scholar] [CrossRef]
- Rahman, M.M.; Sultana, N.; Velayutham, E. Renewable energy, energy intensity and carbon reduction: Experience of large emerging economies. Renew. Energy 2022, 184, 252–265. [Google Scholar] [CrossRef]
- Pang, G.; Ding, Z.; Shen, X. Spillover effect of energy intensity reduction targets on carbon emissions in China. Front. Environ. Sci. 2023, 11, 1054272. [Google Scholar] [CrossRef]
- Namahoro, J.P.; Wu, Q.; Zhou, N.; Xue, S. Impact of energy intensity, renewable energy, and economic growth on CO2 emissions: Evidence from Africa across regions and income levels. Renew. Sustain. Energy Rev. 2021, 147, 111233. [Google Scholar] [CrossRef]
- Nwani, C.; Bekun, F.V.; Gyamfi, B.A.; Effiong, E.L.; Alola, A.A. Toward sustainable use of natural resources: Nexus between resource rents, affluence, energy intensity and carbon emissions in developing and transition economies. In Natural Resources Forum; Blackwell Publishing Ltd.: Oxford, UK, 2023; pp. 155–176. [Google Scholar] [CrossRef]
- Zhang, X.; Shi, X.; Khan, Y.; Khan, M.; Naz, S.; Hassan, T.; Wu, C.; Rahman, T. The impact of energy intensity, energy productivity and natural resource rents on carbon emissions in Morocco. Sustainability 2023, 15, 6720. [Google Scholar] [CrossRef]
- Yang, Z.; Cai, J.; Lu, Y.; Zhang, B. The impact of economic growth, industrial transition, and energy intensity on carbon dioxide emissions in China. Sustainability 2022, 14, 4884. [Google Scholar] [CrossRef]
- Naqvi, S.A.A.; Shah, S.A.R.; Abbas, N. Nexus between urbanization, emission, openness, and energy intensity: Panel study across income groups. Environ. Sci. Pollut. Res. 2020, 27, 24253–24271. [Google Scholar] [CrossRef] [PubMed]
- Vo, D.H.; Ho, C.M. Foreign investment, economic growth, and environmental degradation since the 1986 “Economic Renovation” in Vietnam. Environ. Sci. Pollut. Res. 2021, 28, 29795–29805. [Google Scholar] [CrossRef]
- Ali, W.; Gohar, R.; Chang, B.H.; Wong, W.K. Revisiting the impacts of globalization, renewable energy consumption, and economic growth on environmental quality in South Asia. Adv. Decis. Sci. 2022, 26, 75–98. [Google Scholar] [CrossRef]
- Kihombo, S.; Vaseer, A.I.; Ahmed, Z.; Chen, S.; Kirikkaleli, D.; Adebayo, T.S. Is there a tradeoff between financial globalization, economic growth, and environmental sustainability? An advanced panel analysis. Environ. Sci. Pollut. Res. 2022, 29, 3983–3993. [Google Scholar] [CrossRef] [PubMed]
- Joof, F.; Samour, A.; Ali, M.; Tursoy, T.; Haseeb, M.; Hossain, M.E.; Kamal, M. Symmetric and asymmetric effects of gold, and oil price on environment: The role of clean energy in China. Resour. Policy 2023, 81, 103443. [Google Scholar] [CrossRef]
- Luo, J.; Ali, S.A.; Aziz, B.; Aljarba, A.; Akeel, H.; Hanif, I. Impact of natural resource rents and economic growth on environmental degradation in the context of COP-26: Evidence from low-income, middle-income, and high-income Asian countries. Resour. Policy 2023, 80, 103269. [Google Scholar] [CrossRef]
- Akbostancı, E.; Türüt-Aşık, S.; Tunç, G.İ. The relationship between income and environment in Turkey: Is there an environmental Kuznets curve? Energy Policy 2009, 37, 861–867. [Google Scholar] [CrossRef]
- Wang, Z.; Ye, X. Re-examining environmental Kuznets curve for China’s city-level carbon dioxide (CO2) emissions. Spat. Stat. 2017, 21, 377–389. [Google Scholar] [CrossRef]
- Ali, M.; Seraj, M.; Turuc, F.; Tursoy, T.; Uktamov, K.F. Green finance investment and climate change mitigation in OECD-15 European countries: RALS and QARDL evidence. Environ. Dev. Sustain. 2024, 26, 27409–27429. [Google Scholar] [CrossRef]
- Samour, A.; Jahanger, A.; Ali, M.; Joof, F.; Tursoy, T. Renewable energy, natural resources, technological innovation, and consumption-based carbon emissions in China: Tracking environmental neutrality. In Natural Resources Forum; Blackwell Publishing Ltd.: Oxford, UK, 2023. [Google Scholar] [CrossRef]
- Joof, F.; Samour, A.; Tursoy, T.; Ali, M. Climate change, insurance market, renewable energy, and biodiversity: Double-materiality concept from BRICS countries. Environ. Sci. Pollut. Res. 2023, 30, 28676–28689. [Google Scholar] [CrossRef]
- Samour, A.; Joof, F.; Ali, M.; Tursoy, T. Do financial development and renewable energy shocks matter for environmental quality: Evidence from top 10 emitting emissions countries. Environ. Sci. Pollut. Res. 2023, 30, 78879–78890. [Google Scholar] [CrossRef]
- Azam, M.; Uddin, I.; Saqib, N. The determinants of life expectancy and environmental degradation in Pakistan: Evidence from ARDL bounds test approach. Environ. Sci. Pollut. Res. 2023, 30, 2233–2246. [Google Scholar] [CrossRef] [PubMed]
- Khan, I.; Hou, F.; Le, H.P. The impact of natural resources, energy consumption, and population growth on environmental quality: Fresh evidence from the United States of America. Sci. Total Environ. 2021, 754, 142222. [Google Scholar] [CrossRef]
- Pata, U.K.; Yurtkuran, S.; Ahmed, Z.; Kartal, M.T. Do life expectancy and hydropower consumption affect ecological footprint? Evidence from novel augmented and dynamic ARDL approaches. Heliyon 2023, 9, e19567. [Google Scholar] [CrossRef] [PubMed]
- Wang, Q.; Li, L. The effects of population aging, life expectancy, unemployment rate, population density, per capita GDP, urbanization on per capita carbon emissions. Sustain. Prod. Consum. 2021, 28, 760–774. [Google Scholar] [CrossRef]
- Mahalik, M.K.; Padhan, H.; Patel, G.; Mishra, S.; Chyrmang, R. The role of gender life expectancy in environmental degradation: New insights for the BRICS economies. Environ. Dev. Sustain. 2023, 26, 9305–9334. [Google Scholar] [CrossRef]
- Sinha, A.; Sengupta, T.; Alvarado, R. Interplay between technological innovation and environmental quality: Formulating the SDG policies for next 11 economies. J. Clean. Prod. 2020, 242, 118549. [Google Scholar] [CrossRef]
- Balsalobre-Lorente, D.; Ibáñez-Luzón, L.; Usman, M.; Shahbaz, M. The environmental Kuznets curve, based on the economic complexity, and the pollution haven hypothesis in PIIGS countries. Renew. Energy 2022, 185, 1441–1455. [Google Scholar] [CrossRef]
- Acheampong, A.O. Modelling for insight: Does financial development improve environmental quality? Energy Econ. 2019, 83, 156–179. [Google Scholar] [CrossRef]
- Ali, M.; Seraj, M. Nexus between energy consumption and carbon dioxide emission: Evidence from 10 highest fossil fuel and 10 highest renewable energy-using economies. Environ. Sci. Pollut. Res. 2022, 29, 87901–87922. [Google Scholar] [CrossRef]
- Liang, L.; Wang, Z.; Li, J. The effect of urbanization on environmental pollution in rapidly developing urban agglomerations. J. Clean. Prod. 2019, 237, 117649. [Google Scholar] [CrossRef]
- Im, K.S.; Lee, J.; Tieslau, M.A. More Powerful Unit Root Tests with Non-Normal Errors; Springer: New York, NY, USA, 2014. [Google Scholar] [CrossRef]
- Engle, R.F.; Granger, C.W. Co-integration and error correction: Representation, estimation, and testing. Econom. J. Econom. Soc. 1987, 251–276. [Google Scholar] [CrossRef]
- Lee, H.; Lee, J.; Im, K. More powerful cointegration tests with non-normal errors. Stud. Nonlinear Dyn. Econom. 2015, 19, 397–413. [Google Scholar] [CrossRef]
- Ali, M.; Joof, F.; Samour, A.; Tursoy, T.; Balsalobre-Lorente, D.; Radulescu, M. Testing the impacts of renewable energy, natural resources rent, and technological innovation on the ecological footprint in the USA: Evidence from Bootstrapping ARDL. Resour. Policy 2023, 86, 104139. [Google Scholar] [CrossRef]
- Meng, M.; Strazicich, M.C.; Lee, J. Hysteresis in unemployment? Evidence from linear and nonlinear unit root tests and tests with non-normal errors. Empir. Econ. 2017, 53, 1399–1414. [Google Scholar] [CrossRef]
- Zhang, D.; Wang, Q.; Yang, Y. Cure-all or curse? A meta-regression on the effect of tourism development on poverty alleviation. Tour. Manag. 2023, 94, 104650. [Google Scholar] [CrossRef]
- Hu, K.; Raghutla, C.; Chittedi, K.R.; Zhang, R.; Koondhar, M.A. The effect of energy resources on economic growth and carbon emissions: A way forward to carbon neutrality in an emerging economy. J. Environ. Manag. 2021, 298, 113448. [Google Scholar] [CrossRef]
- Li, J.; Li, S. Energy investment, economic growth and carbon emissions in China—Empirical analysis based on spatial Durbin model. Energy Policy 2020, 140, 111425. [Google Scholar] [CrossRef]
- Osadume, R.; University, E.O. Impact of economic growth on carbon emissions in selected West African countries, 1980–2019. J. Money Bus. 2021, 1, 8–23. [Google Scholar] [CrossRef]
- Ozcan, B.; Tzeremes, P.G.; Tzeremes, N.G. Energy consumption, economic growth and environmental degradation in OECD countries. Econ. Model. 2020, 84, 203–213. [Google Scholar] [CrossRef]
- Schröder, E.; Storm, S. Economic growth and carbon emissions: The road to “hothouse earth” is paved with good intentions. Int. J. Political Econ. 2020, 49, 153–173. [Google Scholar] [CrossRef]
- Xue, C.; Shahbaz, M.; Ahmed, Z.; Ahmad, M.; Sinha, A. Clean energy consumption, economic growth, and environmental sustainability: What is the role of economic policy uncertainty? Renew. Energy 2022, 184, 899–907. [Google Scholar] [CrossRef]
- Zhang, H. Technology innovation, economic growth and carbon emissions in the context of carbon neutrality: Evidence from BRICS. Sustainability 2021, 13, 11138. [Google Scholar] [CrossRef]
- Liu, M.; Chen, Z.; Sowah Jr, J.K.; Ahmed, Z.; Kirikkaleli, D. The dynamic impact of energy productivity and economic growth on environmental sustainability in South European countries. Gondwana Res. 2023, 115, 116–127. [Google Scholar] [CrossRef]
Symbol | Variable Name | Unit of Calculation (Measurement) | Source |
---|---|---|---|
CO2 | Carbon emissions | “CO2 emissions (kt)”. | WB |
TOUR | Tourism | “International tourism, number of arrivals”. | WB |
ENERGY | Energy intensity | “Ratio between energy supply and gross domestic product measured at purchasing power parity”. | WB |
GDP | Economic growth | “GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products”. | WB |
LIFE | Life expectancy | “Life expectancy indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life”. | WB |
POP | Population | “Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship”. | WB |
Variables | ADF | RALS-ADF | |
---|---|---|---|
CO2 | −2.49 | −1.87 | 0.51 |
TOUR | −4.12 *** | −2.99 | 0.74 |
ENERGY | −2.33 | −2.15 | 0.63 |
GDP | −3.85 *** | −7.39 *** | 0.91 |
LIFE | −3.98 *** | −2.83 | 0.63 |
POP | −3.63 *** | −1.19 | 0.55 |
∆CO2 | −23.04 *** | −54.99 ** | 0.81 |
∆TOUR | −12.14 *** | −26.87 *** | 0.91 |
∆ENERGY | −24.21 *** | −38.55 *** | 0.84 |
∆LIFE | −26.62 ** | −42.55 *** | 0.90 |
∆POP | −23.39 *** | −63.85 *** | 0.86 |
Methods | K | Test Statistics | |
---|---|---|---|
EG | 0 | −5.33 *** | − |
RALS-EG | 0 | −4.73 ** | 0.81 |
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
---|---|---|---|---|
Long-Run Coefficients | ||||
LOG(TOUR) | 0.019 | 0.006 | 3.028 | 0.002 *** |
LOG(ENERGY) | 0.004 | 0.002 | 2.01 | 0.041 ** |
LOG(GDP) | 0.007 | 0.015 | 0.465 | 0.642 |
LOG(LIFE) | 0.070 | 0.158 | 0.441 | 0.659 |
LOG(POP) | 0.023 | 0.010 | 2.247 | 0.025 ** |
Short-Run Coefficients | ||||
ECM (−1) | −0.043 | 0.010 | −4.262 | 0.000 *** |
DLOG(ENERGY) | 0.568 | 0.041 | 13.570 | 0.000 *** |
DLOG(GDP) | 0.853 | 0.024 | 34.305 | 0.000 *** |
DLOG(LIFE) | 0.670 | 0.315 | 2.12 | 0.033 ** |
DLOG(POP) | 0.216 | 0.025 | 8.570 | 0.000 *** |
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Yilmaz, Y.U.; Rezapouraghdam, H.; Kilic, H. The Nexus Between Tourism and Environmental Quality in Countries Most Dependent on Tourism: A RALS Approach to the Cointegration Test. Sustainability 2025, 17, 3943. https://doi.org/10.3390/su17093943
Yilmaz YU, Rezapouraghdam H, Kilic H. The Nexus Between Tourism and Environmental Quality in Countries Most Dependent on Tourism: A RALS Approach to the Cointegration Test. Sustainability. 2025; 17(9):3943. https://doi.org/10.3390/su17093943
Chicago/Turabian StyleYilmaz, Yenilmez Ufuk, Hamed Rezapouraghdam, and Hasan Kilic. 2025. "The Nexus Between Tourism and Environmental Quality in Countries Most Dependent on Tourism: A RALS Approach to the Cointegration Test" Sustainability 17, no. 9: 3943. https://doi.org/10.3390/su17093943
APA StyleYilmaz, Y. U., Rezapouraghdam, H., & Kilic, H. (2025). The Nexus Between Tourism and Environmental Quality in Countries Most Dependent on Tourism: A RALS Approach to the Cointegration Test. Sustainability, 17(9), 3943. https://doi.org/10.3390/su17093943