Abstract
Corruption is often linked with income inequality and its impact on carbon emissions. This study investigates the moderating effect of corruption governance on the relationship between income inequality and carbon emissions. Panel data for 62 countries from 2012 to 2020 were used. We employed a threshold panel regression approach, considering income inequality as the explanatory variable and carbon dioxide emissions as the dependent variable, with corruption governance as the threshold variable. Our findings suggest that enhancing the level of corruption governance can mitigate the CO2 emissions driven by income inequality. Specifically, we found a shift in the impact on CO2 emissions when corruption governance crosses a certain threshold. This study provides insights into how improving corruption governance can help in managing the environmental effects of income inequality.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Adedoyin FF, Alola AA, Bekun FV (2021a) The alternative energy utilization and common regional trade outlook in EU-27: evidence from common correlated effects. Renew Sustain Energy Rev 145:111092. https://doi.org/10.1016/j.rser.2021.111092
Adedoyin FF, Ozturk I, Agboola MO, Agboola PO, Bekun FV (2021b) The implications of renewable and non-renewable energy generating in Sub-Saharan Africa: the role of economic policy uncertainties. Energy Policy 150:112115. https://doi.org/10.1016/j.enpol.2020.112115
Alesina A, Angeletos GM (2005) Fairness and redistribution. Am Econ Rev 95(4):960–980. https://doi.org/10.1257/0002828054825655
Al-Mulali U, Ozturk I (2016) The investigation of environmental Kuznets curve hypothesis in the advanced economies: the role of energy prices. Renew Sustain Energy Rev 54:1622–1631. https://doi.org/10.1016/j.rser.2015.10.131
Alvarez-Herranz A, Balsalobre-Lorente D, Shahbaz M, Cantos JM (2017) Energy innovation and renewable energy consumption in the correction of air pollution levels. Energy Policy 105:386–397. https://doi.org/10.1016/j.enpol.2017.03.009
Ameyaw EE, Parn E, Chan APC, Owusu-Manu D-G, Edwards DJ, Darko A (2017) Corrupt practices in the construction industry: survey of Ghanaian experience. J Manag Eng 33(6):05017006. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000555
Andersson FNG (2023) Income inequality and carbon emissions in the United States 1929-2019. Ecol Econ 204:107633. https://doi.org/10.1016/j.ecolecon.2022.107633
Arminen H, Menegaki AN (2019) Corruption, climate and the energy-environment-growth nexus. Energy Econ 80:621–634. https://doi.org/10.1016/j.eneco.2019.02.009
Ashenafi BB (2022) Greenhouse gas emission widens income inequality in Africa. Environ Sci Pollut Res 29(31):46691–46707. https://doi.org/10.1007/s11356-022-18925-5
Asongu SA, Odhiambo NM (2020) Governance, CO2 emissions and inclusive human development in sub-Saharan Africa. Energy Explor Exploit 38(1):18–36. https://doi.org/10.1177/0144598719835594
Balsalobre-Lorente D, Ibanez-Luzon L, Usman M, Shahbaz M (2022) The environmental Kuznets curve, based on the economic complexity, and the pollution haven hypothesis in PIIGS countries. Renew Energy 185:1441–1455. https://doi.org/10.1016/j.renene.2021.10.059
Breitung J (2001) The local power of some unit root tests for panel data. In: Nonstationary panels, panel cointegration, and dynamic panels. Emerald Group Publishing Limited, pp 161–177
Breitung J, Das S (2005) Panel unit root tests under cross-sectional dependence. Stat Neerl 59(4):414–433. https://doi.org/10.1111/j.1467-9574.2005.00299.x
Breusch TS, Pagan AR (1980) The Lagrange multiplier test and its applications to model specification in econometrics. Rev Econ Stud 47(1):239–253. https://doi.org/10.2307/2297111
Chadee D, Roxas B, Kouznetsov A (2021) Corruption, bribery and innovation in CEE: where is the Link? J Bus Ethics 174(4):747–762. https://doi.org/10.1007/s10551-021-04925-x
Chen H, Hao Y, Li J, Song X (2018) The impact of environmental regulation, shadow economy, and corruption on environmental quality: theory and empirical evidence from China. J Clean Prod 195:200–214. https://doi.org/10.1016/j.jclepro.2018.05.206
Chen S, Liu X, Wang S, Wang P (2023) Regional corruption, foreign trade, and environmental pollution. Sustainability 15(1):859. https://doi.org/10.3390/su15010859
Cheng M, Yao Y (2023) Does anti-corruption contribute to improving environmental pollution in China? Appl Econ 55(32):3766–3787. https://doi.org/10.1080/00036846.2022.2118221
Cole MA (2007) Corruption, income and the environment: an empirical analysis. Ecol Econ 62(3-4):637–647. https://doi.org/10.1016/j.ecolecon.2006.08.003
Damania R, Fredriksson PG, List JA (2003) Trade liberalization, corruption, and environmental policy formation: theory and evidence. J Environ Econ Manage 46(3):490–512. https://doi.org/10.1016/S0095-0696(03)00025-1
Demir C, Cergibozan R, Gok A (2019) Income inequality and CO2 emissions: empirical evidence from Turkey. Energy Environ 30(3):444–461. https://doi.org/10.1177/0958305X18793109
Dincer OC, Fredriksson PG (2018) Corruption and environmental regulatory policy in the United States: does trust matter? Resour Energy Econ 54:212–225. https://doi.org/10.1016/j.reseneeco.2018.10.001
Dobson S, Ramlogan-Dobson C (2012) Why is corruption less harmful to income inequality in Latin America? World Dev 40(8):1534–1545. https://doi.org/10.1016/j.worlddev.2012.04.015
Dong B, Chen Y, Fan C (2022) Local corruption and dividend policy: evidence from China. Financ Res Lett 47:102698. https://doi.org/10.1016/j.frl.2022.102698
Farhani S, Ozturk I (2015) Causal relationship between CO2 emissions, real GDP, energy consumption, financial development, trade openness, and urbanization in Tunisia. Environ Sci Pollut Res 22(20):15663–15676. https://doi.org/10.1007/s11356-015-4767-1
Fhima F, Nouira R, Sekkat K (2023) How does corruption affect sustainable development? A threshold non-linear analysis. Econ Anal Policy 78:505–523. https://doi.org/10.1016/j.eap.2023.03.020
Frees EW (1995) Assessing cross-sectional correlation in panel data. J Econom 69(2):393–414. https://doi.org/10.1016/0304-4076(94)01658-M
Frutos-Bencze D, Bukkavesa K, Satimanon M (2023) The nexus between pollution and income inequality: the role of education, corruption, and industrial output. https://doi.org/10.21203/rs.3.rs-2800061/v1
Gaechter S, Schulz JF (2016) Intrinsic honesty and the prevalence of rule violations across societies. Nature 531(7595):496. https://doi.org/10.1038/nature17160
Gerrish E, Watkins SL (2018) The relationship between urban forests and income: a meta-analysis. Landsc Urban Plan 170:293–308. https://doi.org/10.1016/j.landurbplan.2017.09.005
Giannetti M, Liao G, You J, Yu X (2021) The externalities of corruption: evidence from entrepreneurial firms in China. Rev Financ 25(3):629–667. https://doi.org/10.1093/rof/rfaa038
Glaeser E, Scheinkman J, Shleifer A (2003) The injustice of inequality. J Monet Econ 50(1):199–222. https://doi.org/10.1016/S0304-3932(02)00204-0
Grossman, G. M., & Krueger, A. B. (1995). Economic growth and the environment. Q J Econ, 110(2), 353-377 %U https://doi.org/10.2307/2118443.
Gyamfi BA, Agozie DQ, Bekun FV (2022) Can technological innovation, foreign direct investment and natural resources ease some burden for the BRICS economies within current industrial era? Technol Soc 70:102037. https://doi.org/10.1016/j.techsoc.2022.102037
Hadri K (2000) Testing for stationarity in heterogeneous panel data. Econom J 3(2):148–161 Royal Economic Society 2000. https://doi.org/10.1111/1368-423X.00043
Halicioglu F (2009) An econometric study of CO2 emissions, energy consumption, income and foreign trade in Turkey. Energy Policy 37(3):1156–1164. https://doi.org/10.1016/j.enpol.2008.11.012
Handayani BD, Yanto H, Pujiati A, Ridzuan AR, Keshminder JS, Shaari MS (2022) The implication of energy consumption, corruption, and foreign investment for sustainability of income distribution in Indonesia. Sustainability 14(23):15915. https://doi.org/10.3390/su142315915
Hansen B, Caner M (2004) Instrumental variable estimation of a threshold model. Economet Theor 20:813–843. https://doi.org/10.1017/S0266466604205011
Hansen BE (1996) Inference when a nuisance parameter is not identified under the null hypothesis. Econometrica 64(2):413–430 https://www.jstor.org/stable/2171789
Hansen BE (1999a) The grid bootstrap and the autoregressive model. Rev Econ Stat 81(4):594–607. https://doi.org/10.1162/003465399558463
Hansen BE (1999b) Threshold effects in non-dynamic panels: estimation, testing, and inference. J Econom 93(2):345–368. https://doi.org/10.1016/S0304-4076(99)00025-1
He X, Adebayo TS, Kirikkaleli D, Umar M (2021) Consumption-based carbon emissions in Mexico: an analysis using the dual adjustment approach. Sustain Prod Consum 27:947–957. https://doi.org/10.1016/j.spc.2021.02.020
Hong S, Han H, Kim CS (2020) World distribution of income for 1970-2010: dramatic reduction in world income inequality during the 2000s. Empir Econ 59(2):765–798. https://doi.org/10.1007/s00181-019-01657-w
Im KS, Pesaran MH, Shin Y (2003) Testing for unit roots in heterogeneous panels. J Econom 115(1):53–74. https://doi.org/10.1016/S0304-4076(03)00092-7
Kanagawa M, Nakata T (2008) Assessment of access to electricity and the socio-economic impacts in rural areas of developing countries. Energy Policy 36(6):2016–2029. https://doi.org/10.1016/j.enpol.2008.01.041
Kasa R, Rethi G, Hauber G, Szegedi K (2023) Simulation of corruption decisions-an agent-based approach. Sustainability 15(3):2561. https://doi.org/10.3390/su15032561
Kaufmann D, Kraay A (2023) The Worldwide Governance Indicators (WGI) project. The worldwide governance indicators. http://info.worldbank.org/governance/wgi
Kaufmann D, Kraay A, Mastruzzi M (2011) The Worldwide Governance Indicators: methodology and analytical issues. Hague J Rule Law 3(2):220–246. https://doi.org/10.1017/S1876404511200046
Khan H, Weili L, Khan I, Han L (2022) The effect of income inequality and energy consumption on environmental degradation: the role of institutions and financial development in 180 countries of the world. Environ Sci Pollut Res 29(14):20632–20649. https://doi.org/10.1007/s11356-021-17278-9
Kong D, Zhu L, Wang X (2022) Anti-corruption and CEO compensation: evidence from a natural experiment in China. Econ Model 106:105697. https://doi.org/10.1016/j.econmod.2021.105697
Kopp T, Nabernegg M (2022) Inequality and environmental impact - can the two be reduced jointly? Ecol Econ 201:107589. https://doi.org/10.1016/j.ecolecon.2022.107589
Le HP, Ozturk I (2020) The impacts of globalization, financial development, government expenditures, and institutional quality on CO2 emissions in the presence of environmental Kuznets curve. Environ Sci Pollut Res 27(18):22680–22697. https://doi.org/10.1007/s11356-020-08812-2
Lehman G, Morton E (2017) Accountability, corruption and social and environment accounting: micro-political processes of change. Account Forum 41(4):281–288. https://doi.org/10.1016/j.accfor.2017.10.004
Li C, Yu Y, Li Q (2021a) Top-income data and income inequality correction in China. Econ Model 97:210–219. https://doi.org/10.1016/j.econmod.2021.01.018
Li R, Wang Q, Liu Y, Jiang R (2021b) Per-capita carbon emissions in 147 countries: the effect of economic, energy, social, and trade structural changes. Sustain Prod Consum 27:1149–1164. https://doi.org/10.1016/j.spc.2021.02.031
Li R, Li L, Wang Q (2022) The impact of energy efficiency on carbon emissions: evidence from the transportation sector in Chinese 30 provinces. Sustain Cities Soc 82:103880. https://doi.org/10.1016/j.scs.2022.103880
Li R, Wang Q, Le L, Hu S, (2023) Do natural resource rent and corruption governance reshape the environmental Kuznets curve for ecological footprint? Evidence from 158 countries. Resour Policy 85:103890. https://doi.org/10.1016/j.resourpol.2023.103890
Lu J, Li H (2023) The impact of environmental corruption on green consumption: a quantitative analysis based on China’s judicial document network and Baidu Index. Socioecon Plann Sci 86:101451. https://doi.org/10.1016/j.seps.2022.101451
Mashhoodi B (2021) Environmental justice and surface temperature: income, ethnic, gender, and age inequalities. Sustain Cities Soc 68:102810. https://doi.org/10.1016/j.scs.2021.102810
Meijering E (2002) A chronology of interpolation: from ancient astronomy to modern signal and image processing. In: Proceedings of the IEEE 90(3):319–342. Presented at the Proceedings of the IEEE. https://doi.org/10.1109/5.993400
Moran PAP (1948) The interpretation of statistical maps. J R Stat Soc B Methodol 10(2):243–251 https://www.jstor.org/stable/2983777
Moscariello N, Pizzo M, Ricciardi G, Mallardo G, Fattorusso P (2022) The anti-corruption compliance models in a multinational company: a single case study. Bus Strategy Environ. https://doi.org/10.1002/bse.3331
Mujtaba A, Jena PK, Bekun FV, Sahu PK (2022) Symmetric and asymmetric impact of economic growth, capital formation, renewable and non-renewable energy consumption on environment in OECD countries. Renew Sustain Energy Rev 160:112300. https://doi.org/10.1016/j.rser.2022.112300
Nasreen S, Anwar S, Ozturk I (2017) Financial stability, energy consumption and environmental quality: evidence from South Asian economies. Renew Sustain Energy Rev 67:1105–1122. https://doi.org/10.1016/j.rser.2016.09.021
Ongo Nkoa BE, Song JS (2022) Does institutional quality increase inequalities in Africa? J Knowl Econ 13(3):1896–1927. https://doi.org/10.1007/s13132-021-00771-4
Opeyemi A, Uchenna E, Simplice A, Evans O (2019) Renewable energy, trade performance and the conditional role of finance and institutional capacity in sub-Sahara African countries. Energy Policy 132:490–498. https://doi.org/10.1016/j.enpol.2019.06.012
Oswald Y, Owen A, Steinberger JK (2020) Large inequality in international and intranational energy footprints between income groups and across consumption categories. Nat Energy 5(3):231–239. https://doi.org/10.1038/s41560-020-0579-8
Owusu EK, Chan APC, Hosseini MR (2020) Impacts of anti-corruption barriers on the efficacy of anti-corruption measures in infrastructure projects: implications for sustainable development. J Clean Prod 246:119078. https://doi.org/10.1016/j.jclepro.2019.119078
Ozokcu S, Ozdemir O (2017) Economic growth, energy, and environmental Kuznets curve. Renew Sustain Energy Rev 72:639–647. https://doi.org/10.1016/j.rser.2017.01.059
Pao H-T, Tsai C-M (2011) Multivariate Granger causality between CO2 emissions, energy consumption, FDI (foreign direct investment) and GDP (gross domestic product): evidence from a panel of BRIC (Brazil, Russian Federation, India, and China) countries. Energy 36(1):685–693. https://doi.org/10.1016/j.energy.2010.09.041
Pesaran MH (2004) General diagnostic tests for cross-sectional dependence in panels. Empir Econ 60(1):13–50. https://doi.org/10.1007/s00181-020-01875-7
Pesaran MH (2015) Testing weak cross-sectional dependence in large panels. Econom Rev 34(6-10):1089–1117. https://doi.org/10.1080/07474938.2014.956623
Rasool SF, Zaman S, Jehan N, Chin T, Khan S, Zaman Qu (2022) Investigating the role of the tech industry, renewable energy, and urbanization in sustainable environment: policy directions in the context of developing economies. Technol Forecast Soc Chang 183:121935. https://doi.org/10.1016/j.techfore.2022.121935
Ren Y-S, Ma C-Q, Apergis N, Sharp B (2021) Responses of carbon emissions to corruption across Chinese provinces. Energy Econ 98:105241 https://doi.org/10.1016/j.eneco.2021.105241
Ridzuan, A. R., Ismail, N. A., & Hamat, A. F. C. (2018). Foreign Direct Investment And Trade Openness: Do They Lead To Sustainable Development In Malaysia? (4).
Safar W (2022) Income inequality and CO2 emissions in France: does income inequality indicator matter? J Clean Prod 370:133457. https://doi.org/10.1016/j.jclepro.2022.133457
Sarkodie SA, Ozturk I (2020) Investigating the environmental Kuznets curve hypothesis in Kenya: a multivariate analysis. Renew Sustain Energy Rev 117:109481. https://doi.org/10.1016/j.rser.2019.109481
Sehrawat M, Singh SK (2021) Do corruption and income inequality play spoilsport in the energy efficiency-growth relationship in BRICS countries? J Quant Econ 19(4):727–746. https://doi.org/10.1007/s40953-021-00244-0
Shahbaz M, Khraief N, Uddin GS, Ozturk I (2014) Environmental Kuznets curve in an open economy: a bounds testing and causality analysis for Tunisia. Renew Sustain Energy Rev 34:325–336. https://doi.org/10.1016/j.rser.2014.03.022
Silal P, Saha D (2021) Impact of national e-participation levels on inclusive human development and environmental performance: the mediating role of corruption control. Gov Inf Q 38(4):101615. https://doi.org/10.1016/j.giq.2021.101615
Solarin SA, Al-Mulali U, Ozturk I (2017) Validating the environmental Kuznets curve hypothesis in India and China: the role of hydroelectricity consumption. Renew Sustain Energy Rev 80:1578–1587. https://doi.org/10.1016/j.rser.2017.07.028
Sovacool BK (2021) Clean, low-carbon but corrupt? Examining corruption risks and solutions for the renewable energy sector in Mexico, Malaysia, Kenya and South Africa. Energ Strat Rev 38:100723. https://doi.org/10.1016/j.esr.2021.100723
Sovacool BK, Hook A, Martiskainen M, Baker L (2019) The whole systems energy injustice of four European low-carbon transitions. Glob Environ Change 58:101958. https://doi.org/10.1016/j.gloenvcha.2019.101958
Stern DI (2004) The rise and fall of the environmental Kuznets curve. World Dev 32(8):1419–1439. https://doi.org/10.1016/j.worlddev.2004.03.004
Tamazian A, Rao BB (2010) Do economic, financial and institutional developments matter for environmental degradation? Evidence from transitional economies. Energy Econ 32(1):137–145. https://doi.org/10.1016/j.eneco.2009.04.004
Tiwari AK, Shahbaz M, Hye QMA (2013) The environmental Kuznets curve and the role of coal consumption in India: cointegration and causality analysis in an open economy. Renew Sustain Energy Rev 18:519–527. https://doi.org/10.1016/j.rser.2012.10.031
Tong H, Lim KS (1980) Threshold autoregression, limit cycles and cyclical data. J R Stat Soc B Methodol 42(3):245–268. https://doi.org/10.1111/j.2517-6161.1980.tb01126.x
Tran QT (2021) Local corruption and dividend policy: evidence from Vietnam. Econ Anal Policy 70:195–205. https://doi.org/10.1016/j.eap.2021.02.011
Uzar U, Eyuboglu K (2019) The nexus between income inequality and CO2 emissions in Turkey. J Clean Prod 227:149–157. https://doi.org/10.1016/j.jclepro.2019.04.169
Wackernagel M, Hanscom L, Jayasinghe P, Lin D, Murthy A, Neill E et al (2021) The importance of resource security for poverty eradication. Nat Sustain 4(8):731–738. https://doi.org/10.1038/s41893-021-00708-4
Wang Q, Zhang F (2021) The effects of trade openness on decoupling carbon emissions from economic growth – evidence from 182 countries. J Clean Prod 279:123838. https://doi.org/10.1016/j.jclepro.2020.123838
Wang Q, Sun J, Pata UK, Li R, Kartal MT (2023a) Digital economy and carbon dioxide emissions: examining the role of threshold variables. Geosci Front 101644. https://doi.org/10.1016/j.gsf.2023.101644
Wang Q, Zhang F, Li R (2023b) Free trade and carbon emissions revisited: the asymmetric impacts of trade diversification and trade openness. Sustain Dev, ERP Environment and John Wiley & Sons Ltd. https://doi.org/10.1002/sd.2703
Wang Q, Zhang F, Li R (2023c) Revisiting the environmental Kuznets curve hypothesis in 208 counties: the roles of trade openness, human capital, renewable energy and natural resource rent. Environ Res 216:114637. https://doi.org/10.1016/j.envres.2022.114637
Wang S, Zhang W, Wang H, Wang J, Jiang M-J (2021) How does income inequality influence environmental regulation in the context of corruption? A panel threshold analysis based on Chinese provincial data. Int J Environ Res Public Health 18(15):8050. https://doi.org/10.3390/ijerph18158050.
Wang Z, Danish, Zhang B, Wang B (2018) The moderating role of corruption between economic growth and CO2 emissions: evidence from BRICS economies. Energy 148:506–513. https://doi.org/10.1016/j.energy.2018.01.167
Wei C, Dang J, Liu L, Li C, Tan X, Gu Z (2023) Economic inequality breeds corrupt behaviour. Br J Soc Psychol 62(2):949–971. https://doi.org/10.1111/bjso.12610
Welsch H (2004) Corruption, growth, and the environment: a cross-country analysis. Environ Dev Econ 9:663–693. https://doi.org/10.1017/S1355770X04001500
Wiedmann T, Lenzen M, Keysser LT, Steinberger JK (2020) Scientists' warning on affluence. Nat Commun 11(1). https://doi.org/10.1038/s41467-020-16941-y
World B (2023) World Bank Open Data. Word Bank Open Data. https://data.worldbank.org.cn.
Yang Y, Wang C, Xu H (2022) Challenge or chance? Understanding the impact of anti-corruption campaign on China’s hotel industry. Tour Econ 28(6):1566–1588. https://doi.org/10.1007/s11356-021-18278-5
You W, Li Y, Guo P, Guo Y (2020) Income inequality and CO2 emissions in belt and road initiative countries: the role of democracy. Environ Sci Pollut Res 27(6):6278–6299. https://doi.org/10.1007/s11356-019-07242-z
Zaman K, Shahbaz M, Loganathan N, Raza SA (2016) Tourism development, energy consumption and environmental Kuznets curve: trivariate analysis in the panel of developed and developing countries. Tour Manag 54:275–283. https://doi.org/10.1016/j.tourman.2015.12.001
Zhang M, Liu J, Liu L, Zhou D (2023) Inequality in urban household energy consumption for 30 Chinese provinces. Energy Policy 172:113326. https://doi.org/10.1016/j.enpol.2022.113326
Zhao J, Shen J, Yan J, Yang X, Hao Y, Ran Q (2023) Corruption, market segmentation and haze pollution: empirical evidence from China. J Environ Plan Manag 66(3):642–664. https://doi.org/10.1080/09640568.2021.2001316
Zoundi Z (2017) CO2 emissions, renewable energy and the environmental Kuznets curve, a panel cointegration approach. Renew Sustain Energy Rev 72:1067–1075. https://doi.org/10.1016/j.rser.2016.10.018
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Funding
The author thanks the following funds for their support: National Natural Science Foundation of China (grant no. 72104246).
Author information
Authors and Affiliations
Contributions
QW: conceptualization, methodology, software, data curation, writing—original draft preparation, supervision, and writing—reviewing and editing. ZY: methodology, software, investigation, writing—original draft, and writing—reviewing and editing. RL: conceptualization, methodology, software, investigation, writing—original draft, and writing—reviewing and editing
Corresponding author
Ethics declarations
Ethics approval and consent to participate
Not applicable
Consent for publication
Not applicable
Competing interests
The authors declare no competing interests.
Additional information
Responsible Editor: Ilhan Ozturk
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix
Appendix
Appendix A. Country list
Country name | ||
|---|---|---|
Albania | Estonia | North Macedonia |
Argentina | Finland | Montenegro |
Armenia | France | Netherlands |
Austria | United Kingdom | Norway |
Belgium | Georgia | Panama |
Bulgaria | Greece | Peru |
Belarus | Honduras | Poland |
Bolivia | Croatia | Portugal |
Brazil | Hungary | Paraguay |
Canada | Indonesia | Russian Federation |
Switzerland | Ireland | El Salvador |
China | Iran, Islamic Rep | Serbia |
Colombia | Iceland | Slovak Republic |
Costa Rica | Israel | Slovenia |
Cyprus | Italy | Sweden |
Czech Republic | Kazakhstan | Thailand |
Germany | Kyrgyz Republic | Turkey |
Denmark | Lithuania | Ukraine |
Dominican Republic | Luxembourg | Uruguay |
Ecuador | Latvia | United States |
Spain | Moldova | |
Appendix B. Unit root test in variables order
Breitung test | HT test | IPS test | ||||
|---|---|---|---|---|---|---|
Statistic | p-value | Statistic | p-value | Statistic | p-value | |
CO2 | 0.4801 | 0.6844 | 0.8994 | 1.0000 | −3.4299 | 0.0003 |
Gini | 2.8438 | 0.9978 | 0.5736 | 0.0005 | −0.5046 | 0.3069 |
top10 | 2.1794 | 0.9854 | 0.4913 | 0.0000 | −2.2969 | 0.0108 |
top20 | 2.7998 | 0.9974 | 0.5626 | 0.0002 | 0.1882 | 0.5746 |
cce | 2.2666 | 0.9883 | 0.7014 | 0.5144 | −1.5519 | 0.0603 |
lnGDP | −0.8043 | 0.2106 | 0.8026 | 0.9963 | 4.6330 | 1.0000 |
lnGDP2 | −0.8358 | 0.2016 | 0.8010 | 0.9958 | 4.9734 | 1.0000 |
rnew | −0.6066 | 0.2721 | 0.8180 | 0.9990 | 1.7424 | 0.9593 |
ind | 0.5080 | 0.6943 | 0.6276 | 0.0295 | −1.3481 | 0.0888 |
open | 6.4184 | 1.0000 | 0.7468 | 0.8888 | −1.8549 | 0.0318 |
elec | 0.5726 | 0.7166 | −2.2687 | 0.0116 | 10.3114 | 1.0000 |
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Wang, Q., Yang, Z. & Li, R. Impact of income inequality on carbon emissions: a matter of corruption governance. Environ Sci Pollut Res 31, 5173–5189 (2024). https://doi.org/10.1007/s11356-023-31190-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-023-31190-4