IDEAS home Printed from https://ideas.repec.org/a/spr/qualqt/v53y2019i4d10.1007_s11135-019-00848-y.html
   My bibliography  Save this article

Cooperation, diffusion of technology and environmental protection: a new index

Author

Listed:
  • Cristian Barra

    (University of Salerno)

  • Giovanna Bimonte

    (University of Salerno)

  • Luigi Senatore

    (University of Salerno)

Abstract

There are various types of environmental indexes or indicators in the literature. In this paper, we propose a new index that is able to point out the important relationship between environmental protection and investments in innovation processes. We identify the index with the acronym EICI (environmental innovation comparative index). This new empirical tool can represent a new way to illustrate how the level of innovation can determine different levels of air pollution in the world. We use generalized method of moments (GMM) and ordinary least squares (OLS) models to investigate how this new index impacts the variations in greenhouse gas emissions and we underline some fundamental policy implications. Considering the levels of the EICI and the empirical analysis of the role of this index then we conclude that enforcing new environmental agreements with some fundamental rules, as the incentive to reduce the technological gaps among the countries, is crucial to protect the environment and at same time stimulate the investment for innovation in all countries of the world.

Suggested Citation

  • Cristian Barra & Giovanna Bimonte & Luigi Senatore, 2019. "Cooperation, diffusion of technology and environmental protection: a new index," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(4), pages 1913-1940, July.
    • Handle: RePEc:spr:qualqt:v:53:y:2019:i:4:d:10.1007_s11135-019-00848-y
    DOI: 10.1007/s11135-019-00848-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11135-019-00848-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11135-019-00848-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Say, Nuriye Peker & Yucel, Muzaffer, 2006. "Energy consumption and CO2 emissions in Turkey: Empirical analysis and future projection based on an economic growth," Energy Policy, Elsevier, vol. 34(18), pages 3870-3876, December.
    2. Li, Aijun & Du, Nan & Wei, Qian, 2014. "The cross-country implications of alternative climate policies," Energy Policy, Elsevier, vol. 72(C), pages 155-163.
    3. Hamilton, Kirk & Clemens, Michael, 1999. "Genuine Savings Rates in Developing Countries," The World Bank Economic Review, World Bank, vol. 13(2), pages 333-356, May.
    4. Maurice J. G. Bun & Frank Windmeijer, 2010. "The weak instrument problem of the system GMM estimator in dynamic panel data models," Econometrics Journal, Royal Economic Society, vol. 13(1), pages 95-126, February.
    5. Holtz-Eakin, Douglas & Selden, Thomas M., 1995. "Stoking the fires? CO2 emissions and economic growth," Journal of Public Economics, Elsevier, vol. 57(1), pages 85-101, May.
    6. Lee, Jaegul & Veloso, Francisco M. & Hounshell, David A., 2011. "Linking induced technological change, and environmental regulation: Evidence from patenting in the U.S. auto industry," Research Policy, Elsevier, vol. 40(9), pages 1240-1252.
    7. Popp, David, 2006. "International innovation and diffusion of air pollution control technologies: the effects of NOX and SO2 regulation in the US, Japan, and Germany," Journal of Environmental Economics and Management, Elsevier, vol. 51(1), pages 46-71, January.
    8. Im, Kyung So & Pesaran, M. Hashem & Shin, Yongcheol, 2003. "Testing for unit roots in heterogeneous panels," Journal of Econometrics, Elsevier, vol. 115(1), pages 53-74, July.
    9. Nick Hanley & Louis Dupuy & Eoin McLaughlin, 2015. "Genuine Savings And Sustainability," Journal of Economic Surveys, Wiley Blackwell, vol. 29(4), pages 779-806, September.
    10. Richmond, Amy K. & Kaufmann, Robert K., 2006. "Is there a turning point in the relationship between income and energy use and/or carbon emissions?," Ecological Economics, Elsevier, vol. 56(2), pages 176-189, February.
    11. Hayakawa, Kazuhiko, 2016. "Improved GMM estimation of panel VAR models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 240-264.
    12. Luzzati, T. & Orsini, M., 2009. "Investigating the energy-environmental Kuznets curve," Energy, Elsevier, vol. 34(3), pages 291-300.
    13. Shafik, Nemat, 1994. "Economic Development and Environmental Quality: An Econometric Analysis," Oxford Economic Papers, Oxford University Press, vol. 46(0), pages 757-773, Supplemen.
    14. Hayakawa, Kazuhiko, 2007. "Small sample bias properties of the system GMM estimator in dynamic panel data models," Economics Letters, Elsevier, vol. 95(1), pages 32-38, April.
    15. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
    16. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    17. Kazuhiko Hayakawa, 2009. "First Difference or Forward Orthogonal Deviation- Which Transformation Should be Used in Dynamic Panel Data Models?: A Simulation Study," Economics Bulletin, AccessEcon, vol. 29(3), pages 2008-2017.
    18. Nick Hanley, 2000. "Macroeconomic Measures of ‘Sustainability’," Journal of Economic Surveys, Wiley Blackwell, vol. 14(1), pages 1-30, February.
    19. Yao, Xin & Zhou, Hongchen & Zhang, Aizhen & Li, Aijun, 2015. "Regional energy efficiency, carbon emission performance and technology gaps in China: A meta-frontier non-radial directional distance function analysis," Energy Policy, Elsevier, vol. 84(C), pages 142-154.
    20. Windmeijer, Frank, 2005. "A finite sample correction for the variance of linear efficient two-step GMM estimators," Journal of Econometrics, Elsevier, vol. 126(1), pages 25-51, May.
    21. Eoin McLaughlin & Nick Hanley & David Greasley & Jan Kunnas & Les Oxley & Paul Warde, 2014. "Historical wealth accounts for Britain: progress and puzzles in measuring the sustainability of economic growth," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 30(1), pages 44-69.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yi Chen & Yinrong Chen & Kun Chen & Min Liu, 2023. "Research Progress and Hotspot Analysis of Residential Carbon Emissions Based on CiteSpace Software," IJERPH, MDPI, vol. 20(3), pages 1-19, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Barra, Cristian & Zotti, Roberto, 2016. "Investigating the impact of national income on environmental pollution. International evidence," MPRA Paper 74149, University Library of Munich, Germany.
    2. Cristian Barra & Roberto Zotti, 2018. "Investigating the non-linearity between national income and environmental pollution: international evidence of Kuznets curve," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 20(1), pages 179-210, January.
    3. Fang, Wen Shwo & Miller, Stephen M. & Yeh, Chih-Chuan, 2012. "The effect of ESCOs on energy use," Energy Policy, Elsevier, vol. 51(C), pages 558-568.
    4. Hiroki Iwata & Keisuke Okada, 2014. "Greenhouse gas emissions and the role of the Kyoto Protocol," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 16(4), pages 325-342, October.
    5. Vieira, Flávio & MacDonald, Ronald & Damasceno, Aderbal, 2012. "The role of institutions in cross-section income and panel data growth models: A deeper investigation on the weakness and proliferation of instruments," Journal of Comparative Economics, Elsevier, vol. 40(1), pages 127-140.
    6. Hayakawa, Kazuhiko, 2019. "Alternative over-identifying restriction test in the GMM estimation of panel data models," Econometrics and Statistics, Elsevier, vol. 10(C), pages 71-95.
    7. Sebastian Kripfganz & Claudia Schwarz, 2019. "Estimation of linear dynamic panel data models with time‐invariant regressors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(4), pages 526-546, June.
    8. Kazuhiko Hayakawa & M. Hashem Pesaran, 2012. "Robust Standard Errors in Transformed Likelihood Estimation of Dynamic Panel Data Models," Working Paper series 38_12, Rimini Centre for Economic Analysis.
    9. Kais, Saidi & Sami, Hammami, 2016. "An econometric study of the impact of economic growth and energy use on carbon emissions: Panel data evidence from fifty eight countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1101-1110.
    10. Kruiniger, Hugo, 2013. "Quasi ML estimation of the panel AR(1) model with arbitrary initial conditions," Journal of Econometrics, Elsevier, vol. 173(2), pages 175-188.
    11. Ryan H. Murphy & Colin O’Reilly, 2019. "Applying panel vector autoregression to institutions, human capital, and output," Empirical Economics, Springer, vol. 57(5), pages 1633-1652, November.
    12. BARRA, Cristian & BIMONTE, Giovanna & SENATORE, Luigi, 2016. "Innovation Processes and Environmental Safety," CELPE Discussion Papers 141, CELPE - CEnter for Labor and Political Economics, University of Salerno, Italy.
    13. Hayakawa, Kazuhiko & Pesaran, M. Hashem, 2015. "Robust standard errors in transformed likelihood estimation of dynamic panel data models with cross-sectional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 188(1), pages 111-134.
    14. Fajeau, Maxime, 2021. "Too much finance or too many weak instruments?," International Economics, Elsevier, vol. 165(C), pages 14-36.
    15. Panagiotis Nikolaos Fotis & Victoria Pekka, 2017. "The effect of renewable energy use and economic growth on pollution in the EUROZONE," Economics and Business Letters, Oviedo University Press, vol. 6(3), pages 88-99.
    16. Hayakawa, K. & Pesaran, M.H., 2012. "Robust Standard Errors in Transformed Likelihood Estimation of Dynamic Panel Models," Cambridge Working Papers in Economics 1224, Faculty of Economics, University of Cambridge.
    17. Richard A. Ashley & Xiaojin Sun, 2016. "Subset-Continuous-Updating GMM Estimators for Dynamic Panel Data Models," Econometrics, MDPI, vol. 4(4), pages 1-13, November.
    18. Vlaicu, Razvan & Verhoeven, Marijn & Grigoli, Francesco & Mills, Zachary, 2014. "Multiyear budgets and fiscal performance: Panel data evidence," Journal of Public Economics, Elsevier, vol. 111(C), pages 79-95.
    19. Sandrine Lardic & Virginie Terraza, 2019. "Financial Ratios Analysis in Determination of Bank Performance in the German Banking Sector," International Journal of Economics and Financial Issues, Econjournals, vol. 9(3), pages 22-47.
    20. Chen, Ping-Yu & Chen, Sheng-Tung & Hsu, Chia-Sheng & Chen, Chi-Chung, 2016. "Modeling the global relationships among economic growth, energy consumption and CO2 emissions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 420-431.

    More about this item

    Keywords

    Kyoto agreement; Environmental index; GMM model; OLS model; Environmental policy;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • F18 - International Economics - - Trade - - - Trade and Environment
    • Q51 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Valuation of Environmental Effects

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:qualqt:v:53:y:2019:i:4:d:10.1007_s11135-019-00848-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.