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Does environmental policy stringency influence CO2 emissions in the Asia Pacific region? A nonlinear perspective

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Abstract

Environmental policy stringency (EPS) is widely adopted as the most practical option to tackle the menace of environmental degradation. Therefore, this study investigates the dynamic linkage between EPS and carbon dioxide (CO2) emissions in the most polluted countries of the Asia Pacific Region for the period 1991–2021. For empirical analysis, we have relied on nonlinear panel ARDL methods. In the NARDL analysis, a positive shock in EPS has a significant negative effect on CO2, while a negative shock in EPS has a significant positive impact on CO2 in both the short and long run. Moreover, the growth of human capital and the rise in renewable energy consumption are crucial in improving environmental quality; however, the rise in the region’s economic prosperity makes the region more polluted in the long run. In light of these findings, our study emphasizes the critical role of policymakers in the Asia Pacific region in implementing and maintaining strict environmental policies to effectively control carbon emissions. These policies can complement other mitigation strategies, such as raising environmental awareness and promoting renewable energy consumption.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Abbreviations

EPS:

Environmental policy stringency

ARDL:

Autoregressive distributed lag

NARDL:

Nonlinear autoregressive distributed lag

IPCC:

Intergovernmental Panel on Climate Change

OECD:

Organisation for economic co-operation and development

CO2:

Carbon dioxide emissions

LLC:

Levin, Lin, and Chu

IPS:

Im, Pesaran, and Shin

CS-ARDL:

Cross-sectional ARDL

CS-ARDL:

Cross-sectional NARDL

References

  • Abban AR, Hasan MZ (2021) The causality direction between environmental performance and financial performance in Australian mining companies – a panel data analysis. Resour Policy 70:101894

    Google Scholar 

  • Afshan S, Ozturk I, Yaqoob T (2022) Facilitating renewable energy transition, ecological innovations and stringent environmental policies to improve ecological sustainability: evidence from MM-QR method. Renew Energy 196:151–160

    Google Scholar 

  • Ahmed K, Ahmed S (2018) A predictive analysis of CO2 emissions, environmental policy stringency, and economic growth in China. Environ Sci Pollut Re 25(16):16091–16100

    CAS  Google Scholar 

  • Alsagr N (2023) How environmental policy stringency affects renewable energy investment? Implications for green investment horizons. Utilities Policy 83:101613

    Google Scholar 

  • Alsagr N, van Hemmen S (2021) The impact of financial development and geopolitical risk on renewable energy consumption: evidence from emerging markets. Environ Sci Pollut Res 28(20):25906–25919

    Google Scholar 

  • Asongu SA, Agboola MO, Alola AA, Bekun FV (2020) The criticality of growth, urbanization, electricity and fossil fuel consumption to environment sustainability in Africa. Sci Total Environ 712:136376

    CAS  Google Scholar 

  • Assogbavi KKE, Dées S (2023) Environmental policy and the CO2 emissions embodied in international trade. Environ Resource Econ 84(2):507–527

    Google Scholar 

  • Azhgaliyeva D (2019) Energy storage and renewable energy deployment: empirical evidence from OECD countries. Energy Procedia 158:3647–3651

  • Barro RJ (1991) Economic growth in a cross section of countries. Q J Econ 106(2):407–443

    Google Scholar 

  • Beck S, Mahony M (2018) The IPCC and the new map of science and politics. Wiley Interdiscip Rev Clim 9(6):e547

    Google Scholar 

  • Chishti MZ, Ullah S, Ozturk I, Usman A (2020) Examining the asymmetric effects of globalization and tourism on pollution emissions in South Asia. Environ Sci Pollut Res 27(22):27721–27737

    Google Scholar 

  • Costa-Campi MT, García-Quevedo J, Martínez-Ros E (2017) What are the determinants of investment in environmental R&D? Energy Policy 104:455–465

    Google Scholar 

  • Danish Zhang, J., Wang, B., & Latif, Z. (2019) Towards cross-regional sustainable development: The nexus between information and communication technology, energy consumption, and CO 2 emissions. Sustain Dev 27(5):990–1000

    Google Scholar 

  • Deshuai M, Hui L, Ullah S (2022) Pro-environmental behavior–renewable energy transitions nexus: exploring the role of higher education and information and communications technology diffusion. Front Psychol 13:1010627

    Google Scholar 

  • Du J, Li F, Sun L (2021) Metal–organic frameworks and their derivatives as electrocatalysts for the oxygen evolution reaction. Chem Soc Rev 50(4):2663–2695

    CAS  Google Scholar 

  • Fischer C, Newell RG (2008) Environmental and technology policies for climate mitigation. J Environ Econ Manag 55(2):142–162

    Google Scholar 

  • Hashim H, Ho WS (2011) Renewable energy policies and initiatives for a sustainable energy future in Malaysia. Renew Sustain Energy Rev 15(9):4780–4787

    Google Scholar 

  • Herman KS, Xiang J (2019) Induced innovation in clean energy technologies from foreign environmental policy stringency? Technol Forecast Soc Chang 147:198–207

    Google Scholar 

  • Heutel G (2012) How should environmental policy respond to business cycles? Optimal policy under persistent productivity shocks. Rev Econ Dyn 15(2):244–264

    Google Scholar 

  • Johnstone K (2012) Impro: Improvisation and the theatre. Routledge

  • Kartal MT (2022) The role of consumption of energy, fossil sources, nuclear energy, and renewable energy on environmental degradation in top-five carbon producing countries. Renew Energy 184:871–880

    CAS  Google Scholar 

  • Kartal MT, Ali U, Nurgazina Z (2022a) Asymmetric effect of electricity consumption on CO2 emissions in the USA: analysis of end-user electricity consumption by nonlinear quantile approaches. Environ Sci Pollut Res 29(55):83824–83838

    Google Scholar 

  • Kartal MT, Depren SK, Kirikkaleli D, Depren Ö, Khan U (2022b) Asymmetric and long-run impact of political stability on consumption-based carbon dioxide emissions in Finland: evidence from nonlinear and Fourier-based approaches. J Environ Manage 321:116043

    CAS  Google Scholar 

  • Kartal MT, Kılıç Depren S, Kirikkaleli D (2023) Asymmetric effect of political stability on production-based CO2 emissions in the UK: long-run evidence from nonlinear ARDL and frequency domain causality. Environ Sci Pollut Res 30(12):33886–33897

    Google Scholar 

  • Khan SAR, Sharif A, Golpîra H, Kumar A (2019) A green ideology in Asian emerging economies: from environmental policy and sustainable development. Sustain Dev 27(6):1063–1075

    Google Scholar 

  • Kılıç Depren S, Kartal MT, Kirikkaleli D, & Depren Ö (2023) Effect of political stability on environmental quality: long-run and asymmetric evidence from Iceland by non-linear approaches. Air Quality, Atmosphere & Health, 1–11.

  • Landrigan PJ, Fuller R, Acosta NJ, Adeyi O, Arnold R, Baldé AB,...& Zhong M (2018) The Lancet Commission on pollution and health. The lancet, 391(10119), 462–512.

  • Lanjouw JO, Mody A (1996) Innovation and the international diffusion of environmentally responsive technology. Res Policy 25(4):549–571

    Google Scholar 

  • Lovely M, Popp D (2011) Trade, technology, and the environment: does access to technology promote environmental regulation? J Environ Econ Manag 61(1):16–35

    Google Scholar 

  • Mahalik MK, Mallick H, Padhan H (2021) Do educational levels influence the environmental quality? The role of renewable and non-renewable energy demand in selected BRICS countries with a new policy perspective. Renew Energy 164:419–432

    Google Scholar 

  • OECD, F. A. O. (2022) OECD-FAO agricultural outlook 2022-2031

    Google Scholar 

  • OECD I (2016) Energy and air pollution: world energy outlook special report 2016

    Google Scholar 

  • Pan M, Zhao X, Rosak-Szyrocka J, Mentel G, Truskolaski T (2023) Internet development and carbon emission-reduction in the era of digitalization: where will resource-based cities go? Resour Policy 81:103345

    Google Scholar 

  • Pata UK, Kartal MT (2023) Impact of nuclear and renewable energy sources on environment quality: testing the EKC and LCC hypotheses for South Korea. Nucl Eng Technol 55(2):587–594

    CAS  Google Scholar 

  • Pesaran MH, Shin Y, Smith RP (1999) Pooled mean group estimation of dynamic heterogeneous panels. J Am Stat Assoc 94(446):621–634

    Google Scholar 

  • Porter ME, Van der Linde C (1995) Toward a new conception of the environment-competitiveness relationship. J Econ Perspect 9(4):97–118

    Google Scholar 

  • Reppelin-Hill V (1999) Trade and environment: an empirical analysis of the technology effect in the steel industry. J Environ Econ Manag 38(3):283–301

    Google Scholar 

  • Schlindwein LF, Montalvo C (2023) Energy citizenship: accounting for the heterogeneity of human behaviours within energy transition. Energy Policy 180:113662

    Google Scholar 

  • Shahbaz M, Raghutla C, Song M, Zameer H, Jiao Z (2020) Public-private partnerships investment in energy as new determinant of CO2 emissions: the role of technological innovations in China. Energy Econ 86:104664

    Google Scholar 

  • Shaheen F, Lodhi MS, Rosak-Szyrocka J, Zaman K, Awan U, Asif M,...& Siddique M (2022) Cleaner technology and natural resource management: an environmental sustainability perspective from China. Clean Technologies, 4(3), 584–606.

  • Sharif A, Raza SA, Ozturk I, Afshan S (2019) The dynamic relationship of renewable and nonrenewable energy consumption with carbon emission: a global study with the application of heterogeneous panel estimations. Renew Energy 133:685–691

    Google Scholar 

  • Sharif A, Baris-Tuzemen O, Uzuner G, Ozturk I, Sinha A (2020) Revisiting the role of renewable and non-renewable energy consumption on Turkey’s ecological footprint: evidence from Quantile ARDL approach. Sustain Cities Soc 57:102138

    Google Scholar 

  • Shin Y, Yu B, & Greenwood-Nimmo M (2014) Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In Festschrift in honor of Peter Schmidt (pp 281–314). Springer, New York, NY.

  • Sohail MT, Ullah S, Majeed MT, Usman A, Andlib Z (2021) The shadow economy in South Asia: dynamic effects on clean energy consumption and environmental pollution. Environ Sci Pollut Res 28:29265–29275

    Google Scholar 

  • Summers R, Heston A (1991) The Penn World Table (Mark 5): an expanded set of international comparisons, 1950–1988. Q J Econ 106(2):327–368

    Google Scholar 

  • Turi JA, Rosak-Szyrocka J, Mansoor M, Asif H, Nazir A, Balsalobre-Lorente D (2022) Assessing wind energy projects potential in Pakistan: challenges and way forward. Energies 15(23):9014

    CAS  Google Scholar 

  • Ullah S, Majeed MT, Hafeez M (2019) Education, experience, social network and firm survival: the case of the electrical fittings cluster in Sargodha, Pakistan. Decision 46:267–278

    Google Scholar 

  • Ulussever T, Kılıç Depren S, Kartal MT, Depren Ö (2023) Estimation performance comparison of machine learning approaches and time series econometric models: evidence from the effect of sector-based energy consumption on CO2 emissions in the USA. Environ Sci Pollut Res 30(18):52576–52592

    Google Scholar 

  • Umar M, Mirza N, Hasnaoui JA, Rochoń MP (2022) The nexus of carbon emissions, oil price volatility, and human capital efficiency. Resour Policy 78:102876

    Google Scholar 

  • Usman M, Yaseen MR, Kousar R, Makhdum MSA (2021) Modeling financial development, tourism, energy consumption, and environmental quality: is there any discrepancy between developing and developed countries? Environ Sci Pollut Res 28(41):58480–58501

    Google Scholar 

  • Usman M, Balsalobre-Lorente D, Jahanger A, Ahmad P (2022) Pollution concern during globalization mode in financially resource-rich countries: do financial development, natural resources, and renewable energy consumption matter? Renew Energy 183:90–102

    Google Scholar 

  • Usman M, Farooq M, Wakeel A, Nawaz A, Cheema SA, urRehman H,...& Sanaullah M (2020) Nanotechnology in agriculture: current status, challenges and future opportunities. Science of the Total Environment, 721, 137778

  • Wang C, Pan R, Wan X, Tan Y, Xu L, McIntyre RS,...& Ho C (2020) A longitudinal study on the mental health of general population during the COVID-19 epidemic in China. Brain, behavior, and immunity, 87, 40–48.

  • Wolde-Rufael Y, Mulat-Weldemeskel E (2021) Do environmental taxes and environmental stringency policies reduce CO2 emissions? Evidence from 7 emerging economies. Environ Sci Pollut Res 28(18):22392–22408

    CAS  Google Scholar 

  • World Bank (2022) Management and disclosure of climate-related financial impacts for state-owned enterprises: toolkit for shareholders and regulators. World Bank

  • World Bank Group (2016) World development report 2016: Digital dividends. World Bank Publications

    Google Scholar 

  • Xie P, Xu Y, Tan X, Tan Q (2023) How does environmental policy stringency influence green innovation for environmental managements? J Environ Manage 338:117766

    Google Scholar 

  • Xie X, Liu Y, Liu J, Zhang X, Zou J, Fontes-Garfias CR,...& Shi P Y (2021) Neutralization of SARS-CoV-2 spike 69/70 deletion, E484K and N501Y variants by BNT162b2 vaccine-elicited sera. Nature medicine, 27(4), 620–621.

  • Yenipazarli A (2019) Incentives for environmental research and development: consumer preferences, competitive pressure and emissions taxation. Eur J Oper Res 276(2):757–769

    Google Scholar 

  • Yin Y, Xiong X, Ullah S, Sohail S (2021) Examining the asymmetric socioeconomic determinants of CO2 emissions in China: challenges and policy implications. Environ Sci Pollut Res 28(40):57115–57125

    CAS  Google Scholar 

  • Zhang S, Yao L, Sun A, Tay Y (2019) Deep learning based recommender system: a survey and new perspectives. ACM Comput Surv 52(1):1–38

    Google Scholar 

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Authors and Affiliations

  1. School of Economics and Management, Weifang University, Weifang, 261061, China

    Linlin Liu

  2. School of Economics and Management, Beijing University of Posts and Telecommunications, 10, Xitucheng Road, Haidian District, Beijing, 100876, China

    Linlin Liu & Hong Wu

  3. School of Economics and Management, Weifang Vocational College, Weifang, 261061, China

    Liwei Pang

  4. Institute of Business Management Sciences, University of Agriculture, Faisalabad, Pakistan

    Muhammad Hafeez

  5. Adnan Kassar School of Business, Lebanese American University, Beirut, Lebanon

    Muhammad Hafeez

  6. AKFA University, Tashkent, Uzbekistan

    Raufhon Salahodjaev

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  1. Linlin Liu
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  2. Liwei Pang
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  3. Hong Wu
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  4. Muhammad Hafeez
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  5. Raufhon Salahodjaev
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Contributions

Linlin Liu and Lewei Pang proposed an idea and modeled it. Muhammad Hafeez, Linlin Liu, Hong Wu, and Lewei Pang have analyzed the data and written the complete draft. Lewei Pang and Raufhon Salahodjaev revised and edited the spelling, grammar and language of the manuscript. Linlin Liu and Hong Wu read and approved the final version.

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Correspondence to Linlin Liu or Muhammad Hafeez.

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Liu, L., Pang, L., Wu, H. et al. Does environmental policy stringency influence CO2 emissions in the Asia Pacific region? A nonlinear perspective. Air Qual Atmos Health 16, 2499–2508 (2023). https://doi.org/10.1007/s11869-023-01417-x

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