Search Advanced

Green Finance

2024, Volume 6, Issue 4: 630-648. doi: 10.3934/GF.2024024
Previous Article Next Article
Research article

The effects of environmental patents on renewable energy consumption

  • 1.
    Finance, Money, and Public Administration, Alexandru Ioan Cuza University of Iași, Iași, Romania
  • 2.
    Doctoral School of Economics and Business Administration, Alexandru Ioan Cuza University of Iași, Iași, Romania
  • Received: 16 July 2024 Revised: 08 September 2024 Accepted: 25 September 2024 Published: 23 October 2024

  • JEL Codes: O34, Q55, Q56

  • Environmental degradation and energy security are two of policymakers' most crucial concerns, with an increasing emphasis on renewable energy development. Studies regarding the role and influence of environmental technology patents in this context become necessary and can provide the empirical evidence needed for public policy decisions in terms of the benefits they bring compared to other innovation measures. Thus, our aim was to capture the effects of environmental technology patents on renewable energy consumption in OECD Member States over the period 2000–2021. We applied the general dynamic panel model with heterogeneous slopes and interactive fixed effects, controlling for cross – sectional dependence and long-run error – correction models based on (Pesaran, 2015) and implemented by Ditzen (2018), and Blackburne Ⅲ & Frank (2007), respectively. The empirical results demonstrated that environmental technology patents can be interpreted as effective mediating mechanisms for increasing renewable energy consumption, thus contributing to a reorientation of activities and consumption toward sustainable development. In other words, environmental patents appear in the transformation equation as the main trigger for innovation, generating a growing influence as the demand for the use of renewable energy sources increases, thus facilitating the transition process towards a (cleaner) and affordable energy market. Based on these results, we believe that policymakers and regulators should pay more attention to the research financing related to the investment and patenting of new environmental technologies, promoting a policy of encouraging and enforcing green technologies, regardless of their scope of application.

    Citation: Mihaela Onofrei, Bogdan Narcis Fîrțescu, Florin Oprea, Dana Claudia Cojocaru. The effects of environmental patents on renewable energy consumption[J]. Green Finance, 2024, 6(4): 630-648. doi: 10.3934/GF.2024024

    Related Papers:

  • Environmental degradation and energy security are two of policymakers' most crucial concerns, with an increasing emphasis on renewable energy development. Studies regarding the role and influence of environmental technology patents in this context become necessary and can provide the empirical evidence needed for public policy decisions in terms of the benefits they bring compared to other innovation measures. Thus, our aim was to capture the effects of environmental technology patents on renewable energy consumption in OECD Member States over the period 2000–2021. We applied the general dynamic panel model with heterogeneous slopes and interactive fixed effects, controlling for cross – sectional dependence and long-run error – correction models based on (Pesaran, 2015) and implemented by Ditzen (2018), and Blackburne Ⅲ & Frank (2007), respectively. The empirical results demonstrated that environmental technology patents can be interpreted as effective mediating mechanisms for increasing renewable energy consumption, thus contributing to a reorientation of activities and consumption toward sustainable development. In other words, environmental patents appear in the transformation equation as the main trigger for innovation, generating a growing influence as the demand for the use of renewable energy sources increases, thus facilitating the transition process towards a (cleaner) and affordable energy market. Based on these results, we believe that policymakers and regulators should pay more attention to the research financing related to the investment and patenting of new environmental technologies, promoting a policy of encouraging and enforcing green technologies, regardless of their scope of application.



    加载中


    [1] Adams S, Nsiah C (2019) Reducing carbon dioxide emissions; Does renewable energy matter? Sci Total Environ 693: 133288. https://doi.org/10.1016/j.scitotenv.2019.07.094 doi: 10.1016/j.scitotenv.2019.07.094
    [2] Adebayo TS, Udemba EN, Ahmed Z, et al. (2021) Determinants of consumption-based carbon emissions in Chile: An application of non-linear ARDL. Environ Sci Pollut R 28: 43908–43922. https://doi.org/10.1007/s11356-021-13830-9 doi: 10.1007/s11356-021-13830-9
    [3] Ahn SC, Horenstein AR (2013) Eigenvalue Ratio Test for the Number of Factors. Econometrica 81: 1203–1227. https://doi.org/10.3982/ECTA8968 doi: 10.3982/ECTA8968
    [4] Alataş S (2022) Do environmental technologies help to reduce transport sector CO2 emissions? Evidence from the EU15 countries. Res Transp Econ 91: 101047. https://doi.org/10.1016/j.retrec.2021.101047 doi: 10.1016/j.retrec.2021.101047
    [5] Alnour M, Önden A, Hasseb M, et al. (2024) The Asymmetric Role of Financial Commitments to Renewable Energy Projects, Public R&D Expenditure, and Energy Patents in Sustainable Development Pathways. Sustainability 16: 5503. https://doi.org/doi.org/10.3390/su16135503 doi: 10.3390/su16135503
    [6] Alofaysan H, Radulescu M, Dembińska I, et al. (2024) The effect of digitalization and green technology innovation on energy efficiency in the European Union. Energ Explor Exploit 42: 1747–1762. https://doi.org/10.1177/01445987241253621 doi: 10.1177/01445987241253621
    [7] Ang TZ, Salem M, Kamarol M, et al. (2022) A comprehensive study of renewable energy sources: Classifications, challenges and suggestions. Energy Strateg Rev 43: 100939. https://doi.org/10.1016/j.esr.2022.100939 doi: 10.1016/j.esr.2022.100939
    [8] Aslan A, Apergis N, Yildirim S (2014) Causality between energy consumption and GDP in the U.S.: Evidence from wavelet analysis. Front Energy 8: 1–8. https://doi.org/10.1007/s11708-013-0290-6 doi: 10.1007/s11708-013-0290-6
    [9] Bai J, Ng S (2002) Determining the Number of Factors in Approximate Factor Models. Econometrica 70: 191–221. https://doi.org/10.1111/1468-0262.00273 doi: 10.1111/1468-0262.00273
    [10] Bakry W, Mallik G, Nghiem XH, et al. (2023) Is green finance really "green"? Examining the long-run relationship between green finance, renewable energy and environmental performance in developing countries. Renew Energ 208: 341–355. https://doi.org/10.1016/j.renene.2023.03.020 doi: 10.1016/j.renene.2023.03.020
    [11] Baron J, Li C, Nasirov S (2018) Joining Standards Organizations: The Role of R&D Expenditures, Patents, and Product-Market Position. SSRN Electronic J. https://doi.org/10.2139/ssrn.3287475 doi: 10.2139/ssrn.3287475
    [12] Barra C, Bimonte G, Senatore L (2019) Cooperation, diffusion of technology and environmental protection: A new index. Qual Quant 53: 1913–1940. https://doi.org/10.1007/s11135-019-00848-y doi: 10.1007/s11135-019-00848-y
    [13] Blackburne Ⅲ EF, Frank MW (2007) Estimation of Nonstationary Heterogeneous Panels. Stata J 7: 197–208. https://doi.org/10.1177/1536867X0700700204 doi: 10.1177/1536867X0700700204
    [14] Breitung J, Das S (2005) Panel unit root tests under cross-sectional dependence. Stat Neerl 59: 414–433. https://doi.org/10.1111/j.1467-9574.2005.00299.x doi: 10.1111/j.1467-9574.2005.00299.x
    [15] Chiou-Wei SZ, Chen CF, Zhu Z (2008) Economic growth and energy consumption revisited—Evidence from linear and nonlinear Granger causality. Energ Econ 30: 3063–3076. https://doi.org/10.1016/j.eneco.2008.02.002 doi: 10.1016/j.eneco.2008.02.002
    [16] Choi I (2001) Unit root tests for panel data. J Int Money Financ 20: 249–272. https://doi.org/10.1016/S0261-5606(00)00048-6 doi: 10.1016/S0261-5606(00)00048-6
    [17] Coluccia D, Dabić M, Del Giudice M, et al. (2020) R&D innovation indicator and its effects on the market. An empirical assessment from a financial perspective. J Bus Res 119: 259–271. https://doi.org/10.1016/j.jbusres.2019.04.015 doi: 10.1016/j.jbusres.2019.04.015
    [18] de Klerk L, Shlapak M, Shmurak A, et al. (2023) CLIMATE DAMAGE CAUSED BY RUSSIA'S WAR IN UKRAINE. Available from: https://climatefocus.com/wp-content/uploads/2022/11/clim-damage-by-russia-war-12months.pdf.
    [19] Ditzen J (2018) Estimating Dynamic Common-Correlated Effects in Stata. Stata J 18: 585–617. https://journals.sagepub.com/doi/10.1177/1536867X1801800306
    [20] Ditzen J (2022) Illuminating the factor and dependence structure in large panel models. London Stata Conference 2022, Available from: https://ideas.repec.org//p/boc/lsug22/18.html.
    [21] Ditzen J, Reese S (2023) xtnumfac: A battery of estimators for the number of common factors in time series and panel-data models. Stata J 23: 438–454. https://doi.org/10.1177/1536867X231175305 doi: 10.1177/1536867X231175305
    [22] Estevão J, Lopes JD (2024) SDG7 and renewable energy consumption: The influence of energy sources. Technol Forecast Soc 198: 123004. https://doi.org/10.1016/j.techfore.2023.123004 doi: 10.1016/j.techfore.2023.123004
    [23] Fakher HA, Ahmed Z, Acheampong AO, et al. (2023) Renewable energy, nonrenewable energy, and environmental quality nexus: An investigation of the N-shaped Environmental Kuznets Curve based on six environmental indicators. Energy 263: 125660. https://doi.org/10.1016/j.energy.2022.125660 doi: 10.1016/j.energy.2022.125660
    [24] Fan J, Liao Y, Yao J (2015) Power Enhancement in High-Dimensional Cross-Sectional Tests. Econometrica 83: 1497–1541. https://doi.org/10.3982/ECTA12749 doi: 10.3982/ECTA12749
    [25] Gagliardini P, Ossola E, Scaillet O (2019) A diagnostic criterion for approximate factor structure. J Econometrics 212: 503–521. https://doi.org/10.1016/j.jeconom.2019.06.001 doi: 10.1016/j.jeconom.2019.06.001
    [26] Hadri K (2000) Testing for stationarity in heterogeneous panel data. Economet J 3: 148–161. https://doi.org/10.1111/1368-423X.00043 doi: 10.1111/1368-423X.00043
    [27] Harris RDF, Tzavalis E (1999) Inference for unit roots in dynamic panels where the time dimension is fixed. J Econometrics 91: 201–226. https://doi.org/10.1016/S0304-4076(98)00076-1 doi: 10.1016/S0304-4076(98)00076-1
    [28] Hossain MR, Dash DP, Das N, et al. (2024) Green energy transition in OECD region through the lens of economic complexity and environmental technology: A method of moments quantile regression perspective. Appl Energ 365: 123235. https://doi.org/10.1016/j.apenergy.2024.123235 doi: 10.1016/j.apenergy.2024.123235
    [29] Im KS, Pesaran MH, Shin Y (2003) Testing for unit roots in heterogeneous panels. J Econometrics 115: 53–74. https://doi.org/10.1016/S0304-4076(03)00092-7 doi: 10.1016/S0304-4076(03)00092-7
    [30] Juodis A, Reese S (2022) The Incidental Parameters Problem in Testing for Remaining Cross-Section Correlation. J Bus Econ Stat 40: 1191–1203. https://doi.org/10.1080/07350015.2021.1906687 doi: 10.1080/07350015.2021.1906687
    [31] Kang D, Sohn SY (2024) Green efficiency strategy considering cyclical relationships among CO2 emissions, green patents, and green bonds. J Clean Prod 464: 142704. https://doi.org/10.1016/j.jclepro.2024.142704 doi: 10.1016/j.jclepro.2024.142704
    [32] Kirikkaleli D, Sofuoğlu E, Ojekemi O (2023) Does patents on environmental technologies matter for the ecological footprint in the USA? Evidence from the novel Fourier ARDL approach. Geosci Front 14: 101564. https://doi.org/10.1016/j.gsf.2023.101564 doi: 10.1016/j.gsf.2023.101564
    [33] Lanjouw JO, Mody A (1996) Innovation and the international diffusion of environmentally responsive technology. Res Policy 25: 549–571. https://doi.org/10.1016/0048-7333(95)00853-5 doi: 10.1016/0048-7333(95)00853-5
    [34] Levin A, Lin CF, Chu CSJ (2002). Unit root tests in panel data: Asymptotic and finite-sample properties. J Econometrics 108: 1–24. https://doi.org/10.1016/S0304-4076(01)00098-7 doi: 10.1016/S0304-4076(01)00098-7
    [35] Liu WC (2020) The Relationship between Primary Energy Consumption and Real Gross Domestic Product: Evidence from Major Asian Countries. Sustainability 12: 2568. https://doi.org/10.3390/su12062568 doi: 10.3390/su12062568
    [36] Elavarasan RM, Nadarajah M, Pugazhendhi R, et al. (2023) The untold subtlety of energy consumption and its influence on policy drive towards Sustainable Development Goal 7. Appl Energ 334: 120698. https://doi.org/10.1016/j.apenergy.2023.120698 doi: 10.1016/j.apenergy.2023.120698
    [37] 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 Energ 164: 419–432. https://doi.org/10.1016/j.renene.2020.09.090 doi: 10.1016/j.renene.2020.09.090
    [38] Mahalingam B, Orman WH (2018) GDP and energy consumption: A panel analysis of the US. Appl Energ 213: 208–218. https://doi.org/10.1016/j.apenergy.2018.01.036 doi: 10.1016/j.apenergy.2018.01.036
    [39] Makeeva E, Popov K, Teplova O (2024) Patenting for profitability: Green energy innovations and firm performance in BRICS countries. Front Env Sci-Switz 12: 1332792 https://doi.org/10.3389/fenvs.2024.1332792 doi: 10.3389/fenvs.2024.1332792
    [40] Mohammed KS, Pata UK, Serret V, et al. (2024) The role of renewable energy and carbon dioxide emissions on the ESG market in European Union. Manag Decis Econ 45: 5146–5158. https://doi.org/10.1002/mde.4316 doi: 10.1002/mde.4316
    [41] National Center for Science and Engineering Statistics (2024) Invention, Knowledge Transfer, and Innovation. Available from: https://ncses.nsf.gov/pubs/nsb20241/data/table/SINV-K.
    [42] Nguyen TT, Pham TAT, Tram HTX (2020) Role of information and communication technologies and innovation in driving carbon emissions and economic growth in selected G-20 countries. J Environ Manage 261: 110162. https://doi.org/10.1016/j.jenvman.2020.110162 doi: 10.1016/j.jenvman.2020.110162
    [43] Onatski A (2010) Determining the Number of Factors from Empirical Distribution of Eigenvalues. Rev Econ Stat 92: 1004–1016.
    [44] Onwe JC, Bandyopadhyay A, Hamid I, et al. (2023) Environment sustainability through energy transition and globalization in G7 countries: What role does environmental tax play? Renew Energ 218: 119302. https://doi.org/10.1162/REST_a_00043 doi: 10.1162/REST_a_00043
    [45] Oyebanji MO, Castanho RA, Genc SY, et al. (2022) Patents on Environmental Technologies and Environmental Sustainability in Spain. Sustainability 14: 6670. https://doi.org/10.3390/su14116670 doi: 10.3390/su14116670
    [46] Pesaran MH (2015) Testing Weak Cross-Sectional Dependence in Large Panels. Economet Rev 34: 1089–1117. https://doi.org/10.1080/07474938.2014.956623 doi: 10.1080/07474938.2014.956623
    [47] Pesaran MH (2021) General diagnostic tests for cross-sectional dependence in panels. Empir Econ 60: 13–50. https://doi.org/10.1007/s00181-020-01875-7 doi: 10.1007/s00181-020-01875-7
    [48] Pesaran MH, Smith R (1995) Estimating long-run relationships from dynamic heterogeneous panels. J Econometrics 68: 79–113. https://doi.org/10.1016/0304-4076(94)01644-F doi: 10.1016/0304-4076(94)01644-F
    [49] Poumanyvong P, Kaneko S (2010) Does urbanization lead to less energy use and lower CO2 emissions? A cross-country analysis. Ecol Econ 70: 434–444. https://doi.org/10.1016/j.ecolecon.2010.09.029 doi: 10.1016/j.ecolecon.2010.09.029
    [50] Rej S, Nag B, Hossain ME (2024) Foreign Direct Investment, Income Inequality and Pollution Reduction: Policy Trilemma for India. Soc Indic Res 174: 221–248. https://doi.org/10.1007/s11205-024-03390-z doi: 10.1007/s11205-024-03390-z
    [51] Si Mohammed K, Serret V, Jabeur SB, et al. (2024) The role of artificial intelligence and fintech in promoting eco-friendly investments and non-greenwashing practices in the US market. J Environ Manage 359: 120977. https://doi.org/10.1016/j.jenvman.2024.120977 doi: 10.1016/j.jenvman.2024.120977
    [52] Tiwari S, Si Mohammed K (2024) Unraveling the impacts of linear economy, circular economy, green energy and green patents on environmental sustainability: Empirical evidence from OECD countries. Gondwana Res 135: 75–88. https://doi.org/10.1016/j.gr.2024.07.018 doi: 10.1016/j.gr.2024.07.018
    [53] Ullah S, Ozturk I, Majeed MT, et al. (2021) Do technological innovations have symmetric or asymmetric effects on environmental quality? Evidence from Pakistan. J Clean Prod 316: 128239. https://doi.org/10.1016/j.jclepro.2021.128239 doi: 10.1016/j.jclepro.2021.128239
    [54] UNCTAD (2023) Transfer of Technology for Development in Times of Accelerating Change: New perspectives for the multilateral framework. Available from: https://unctad.org/meeting/transfer-technology-development-times-accelerating-change-new-perspectives-multilateral.
    [55] UNFCCC (2022) Sharm el-Sheikh Implementation Plan. Available from: https://unfccc.int/documents/624444.
    [56] Voïta T (2023) COP28: A Tale of Money, Fossil Fuels, and Divisions. Available from: https://www.ifri.org/sites/default/files/migrated_files/documents/atoms/files/ifri_voita_cop28_2023_1.pdf.
    [57] Wang B, Khalid S, Mahmood H (2024) R&D Spending and Economic Policy Uncertainty in Asian Countries: An Advanced Panel Data Estimation Study. J Knowl Econ 15: 4710–4729. https://doi.org/10.1007/s13132-023-01285-x doi: 10.1007/s13132-023-01285-x
    [58] Wang R, Usman M, Radulescu M, et al. (2023) Achieving ecological sustainability through technological innovations, financial development, foreign direct investment, and energy consumption in developing European countries. Gondwana Res 119: 138–152. https://doi.org/10.1016/j.gr.2023.02.023 doi: 10.1016/j.gr.2023.02.023
    [59] World Meteorological Organization (2023) 2023 shatters climate records, with major impacts. Available from: https://wmo.int/news/media-centre/2023-shatters-climate-records-major-impacts.
    [60] Xie Y, Pesaran MH (2022) A Bias-Corrected Cd Test for Error Cross-Sectional Dependence in Panel Data Models with Latent Factors. SSRN 4198155. https://doi.org/10.2139/ssrn.4198155 doi: 10.2139/ssrn.4198155
    [61] Yıldırım DÇ, Esen Ö, Yıldırım S (2022) The nonlinear effects of environmental innovation on energy sector-based carbon dioxide emissions in OECD countries. Technol Forecast Soc 182: 121800. https://doi.org/10.1016/j.techfore.2022.121800 doi: 10.1016/j.techfore.2022.121800
    [62] Zakari A, Oryani B, Alvarado R, et al. (2023) Assessing the impact of green energy and finance on environmental performance in China and Japan. Econ Chang Restruct 56: 1185–1199. https://doi.org/10.1007/s10644-022-09469-2 doi: 10.1007/s10644-022-09469-2
    [63] Zhao P, Zhang M (2018) The impact of urbanisation on energy consumption: A 30-year review in China. Urban Clim 24: 940–953. https://doi.org/10.1016/j.uclim.2017.11.005 doi: 10.1016/j.uclim.2017.11.005
  • GF-06-04-024-s001.pdf
  • Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索
Green Finance
5.5 9.6

Metrics

Article views(832) PDF downloads(119) Cited by(0)

Preview PDF
Download XML
Export Citation
Article outline

Figures and Tables

Tables(4)

Other Articles By Authors

Related pages

Tools

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog