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The Role of Energy Innovation and Corruption in Carbon Emissions: Evidence Based on the EKC Hypothesis

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Energy and Environmental Strategies in the Era of Globalization

Part of the book series: Green Energy and Technology ((GREEN))

Abstract

This study investigates how energy innovations and corruption affect carbon emissions. To this end, a panel data model of 16 selected OECD countries is employed, spanning the period of 1995–2016. The empirical framework falls within the hypothesis of the environmental Kuznets curve (EKC), which explores the relationship between the economic growth and carbon emissions. The empirical results show that when economic systems interact with corruption, positive effects that energy innovations have on environmental quality are reduced. Furthermore, the amount of economic growth needed to limit environmental pollution levels is also distorted. Corruption seems to be pernicious for the environment in the long term, as it limits the stage at which decontamination occurs; i.e., corruption reduces the positive effect generated by measures focused on energy innovation in terms of reducing environmental pollution. These findings are expected to be significant in terms of implementing anti-corruption measures and effective environmental policies, and they call for appropriate policy measures that might limit the effects of corruption on environmental quality.

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Notes

  1. 1.

    The 2012 United Nations Conference on Sustainable Development (or the Rio+20 Conference) recognised corruption as an impediment to effective environmental stewardship: “Corruption is a serious barrier to effective resource mobilization and allocation and diverts resources away from activities that are vital for poverty eradication, the fight against hunger and sustainable development” [114].

  2. 2.

    Porter and van der Linde [98] suggest that strict environmental regulation triggers the innovation and introduction of cleaner technologies and environmental improvements through the innovation effect, rendering production processes and products more efficient.

  3. 3.

    The first set of empirical EKC studies appeared independently in three working papers: an NBER working paper as part of a study on the environmental impacts of NAFTA [40], the World Bank’s 1992 World Development Report [103], and a Development Discussion paper as part of a study developed for the International Labour Organization [85]. Kuznets’ name was attached to the inverted U-relationship between pollution and economic development later on due to its resemblance to Kuznets’ [62] inverted-U relationship between income inequality and economic development. However, Panayotou [85] first coined it as the environmental Kuznets curve.

  4. 4.

    β1 = β2 = β3 = 0 denotes a flat pattern or no relationship between x and y. β1 > 0, β2 = β3 = 0 denotes a monotonic increasing relationship or a linear relationship between x and y. β1 < 0, β2 = β3 = 0 denotes a monotonic decreasing relationship between x and y. β1 > 0, β2 < 0, β3 = 0 denotes an inverted-U relationship, i.e., EKC. β1 < 0, β2 > 0, β3 = 0 supports a U-shaped relationship, while β1 > 0, β2 < 0, β3 > 0 denotes a N-shaped curve. Finally, β1 < 0, β2 > 0, β3 < 0 supports an inverted-N relationship.

  5. 5.

    Austria, Canada, Denmark, Finland, France, Germany, Italy, Japan, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, the UK, and the USA.

  6. 6.

    The Engle–Granger (1987) cointegration test is based on an examination of the residuals of a spurious regression performed using I(1) variables. If the variables are cointegrated, then the residuals should be I(0). On the other hand, if the variables are not cointegrated, then the residuals will be I(1).

  7. 7.

    The problems related to endogeneity between regressors could be solved by using FMOLS [90, 91].

  8. 8.

    The analysis also tested the cubic pattern, but the results for \({\text{CPI}}_{it}^{3}\) are statistically insignificant.

  9. 9.

    Fredriksson et al. [33] test the predictions of a theoretical model by using panel data for 14 OECD countries; their empirical results show that corruption increases energy waste by reducing the stringency of energy regulations. In addition, capital owner lobbying is less successful in larger sectors, but corresponding effects are reduced in highly corrupt countries. On the other hand, worker lobbying is more successful in large sectors unless a country is heavily corrupted.

  10. 10.

    For our estimation of turning points for the cubic model, we employed the following formulation [28]:

    $$Xj = \frac{{ - \beta_{2} \pm \sqrt {\beta_{2}^{2} - 3\beta_{1} \beta_{3} } }}{{3\beta_{3} }},\quad \forall j = 1,\;2$$
    (23)

    For the estimation of turning points, it is necessary to change coefficient β1, as the breaking point at which the function reaches maximum and minimum values is dependent on CPI. When the CPI variable appears in moderate model GDPpc, this is expected to affect the coefficient of the first grade. Therefore, coefficient β1* = (β1 + δ3 * CPI) where CPI takes its median value (70.4) is justified by the asymmetric distribution of that variable. In other words, breaking points of the model can be estimated from the β1* = (0.00089 − 4.07E−6 * 70.4) = 0.000610472 coefficient.

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Author information

Authors and Affiliations

  1. Department of Political Economy and Public Finance, Economic and Business Statistics and Economic Policy, University of Castilla La Mancha, Cuenca, Spain

    Daniel Balsalobre-Lorente

  2. Energy and Sustainable Development, Montpelier Business School, Montpelier, France

    Charbel Jose Chiappetta Jabbour

  3. Department of Applied Economics, International Economy Institute, University of Alicante, Alicante, Spain

    Oana M. Driha

  4. School of Management and Economics, Beijing Institute of Technology, Beijing, China

    Muhammad Shahbaz

  5. COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan

    Muhammad Shahbaz

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  1. Daniel Balsalobre-Lorente
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  3. Charbel Jose Chiappetta Jabbour
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Correspondence to Daniel Balsalobre-Lorente .

Editor information

Editors and Affiliations

  1. School of Management and Economics, Beijing Institute of Technology, Beijing, China

    Muhammad Shahbaz

  2. Universidad de Castilla-La Mancha, Cuenca, Spain

    Daniel Balsalobre

Appendix

Appendix

Sources and constructions of variables

  • GHGpcit: This denotes emissions measured in millions of tons of CO2 per capita for country “i” and for year “t” [81].

  • GDPpcit: This denotes income per capita measured in millions of US dollars at current prices and PPPs for country “i” and for year “t” [81].

  • CPIit—Corruption Perception Index: This denotes corruption levels based on a variable that covers 177 countries and scores them on a scale of 0 (highly corrupt) to 100 (very clean). The Transparency Index (CPIN) developed by Transparency International [112] is a good proxy for corruption for the legislative process and for the enforcement of environmental policies [112].

  • ECit: This denotes electricity consumption measured in Gw/h for country “i” and for year “t” [51].

  • E_RDDit: This denotes public expenditures on energy research development and demonstration (RD&D) measured in millions of US dollars at current prices and PPPs for country “i” and for year “t” [81].

  • CPIit * E_RDDit: This variable captures interactions between CPIit and E_RDDit over GHGit (Eq. 2).

  • CPIit * GDPit: This denotes interaction terms of CPIit and GDPit over GHGit (Eq. 4).

  • LABOURit: Labor productivity is a key driver of economic growth and of changes in living standards. Labor productivity growth implies a higher level of output for every hour worked. Labor productivity is also a key driver of international competitiveness, e.g., as measured by unit labor costs (ULC) [81].

  • INFLATit is the inflation index for consumer prices (annual %) for country i and for year t [123].

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Balsalobre-Lorente, D., Shahbaz, M., Chiappetta Jabbour, C.J., Driha, O.M. (2019). The Role of Energy Innovation and Corruption in Carbon Emissions: Evidence Based on the EKC Hypothesis. In: Shahbaz, M., Balsalobre, D. (eds) Energy and Environmental Strategies in the Era of Globalization. Green Energy and Technology. Springer, Cham. https://doi.org/10.1007/978-3-030-06001-5_11

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