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Better clean or efficient? Panel regressions

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Abstract

Most national and international climate agendas promote energy efficiency and fossil to renewable energy substitution as key future policy directions. This paper surveys macro-energy-emission-output panel assessments and shows that previously estimated carbon response functions present diverging shapes with less evidence on the confounding role of development. This study applies a multivariate regression equation and both Pesaran (1995) and Pesaran (2006) mean group estimators with common correlated effects to illustrative samples of countries with data covering five decades. For all groups, long-run panel coefficients show that energy efficiency improvements associate with larger negative carbon responses than fossil-to-renewable energy shifts. Estimates derived from high-income economies are much smaller in magnitude and significance compared to those of developing countries, which is further corroborated by country-level parameters. This implies that least-energy efficient and -green economies can benefit from a wider set of carbon abatement policies.

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Data availability

Data is available upon request to the authors.

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Codes are available upon request to the authors.

Notes

  1. Such a statement is supported by the IEA (2009) Report: “Increasing energy efficiency, much of which can be achieved through low-cost options, offers the greatest potential for reducing

  2. The recent COVID-19 global outbreak followed by national lockdowns re-emphasized how supply chain processes in small- and large-open economies are intrinsically connected, because they rely on the export-led-growth hypothesis for some of them and are globally trade-dependent for most of them. For an exhaustive review of this topic, see Hobbs (2020).

  3. A trade-off between N and T naturally occurred. Here, we attempted to equitably balance between the costs and benefits of sample size N and time length T selections, keeping in mind that the explicit aim of the paper is to capture long-run structural dynamics in response to some empirical gaps highlighted earlier while surveying the literature. That is, a related discussion can be found in Breitung (2015).

  4. Referring to the initial unit root testing baseline offered in Im et al. (2003).

  5. Available at: https://www.oecd-ilibrary.org/energy/data/iea-world-energy-statistics-and-balances

  6. Available at:https://databank.worldbank.org/source/world-development-indicators

  7. Available at: https://www.oecd-ilibrary.org/energy/data/iea-co2-emissions-from-fuel-combustion-statistics

  8. Low- and middle-income countries composing the panel A are Argentina, Bangladesh, Bolivia, Brazil, Chile, China, Colombia, Egypt, Guatemala, India, Indonesia, Iraq, Iran, Kenya, Malaysia, Mexico, Morocco, Pakistan, the Philippines, Saudi Arabia, South Africa, Sri Lanka, Thailand, Tunisia, Turkey, Uruguay, and Venezuela.

  9. High-income countries are Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Ireland, Italy, Japan, Korea, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, the UK, and the USA.

  10. It differs from energy intensity calculated as the amount of energy used per unit of GDP (i.e., energy used divided by GDP). To this extent, it is commonly said that energy intensity corresponds to a measure of the energy inefficiency of an economy (Chang 2014). In this study, we follow IEA (2014)’s definitionFootnote 11

  11. “Some company is called more energy efficient if it delivers more services for the same energy input or the same services for less energy input.”

  12. An in-depth discussion on trade policy, trade costs, and development can be found in Hoekman and Nicita (2011)

  13. Stern (2004, 2017) and Azhgaliyeva et al. (2020) showed that national carbon emissions trajectories are also confounded by threshold energy intensity levels.

  14. See Kuik and Gerlagh (2003) for a review of the economic mechanisms explaining the emergence of a turning point in the broader income-atmospheric polluting emissions response function.

Abbreviations

3SLS:

Three stages-least-squares estimation

ARDL:

Autoregressive distributed lag bounds

CCEMG:

Common correlated effects mean group estimator from Pesaran (2006)

CGE:

Computable general equilibrium

DHNC:

Dumitrescu-Hurlin non-causality test

DOLS:

Dynamic ordinary least square estimation

ECM:

Error correction model

FE:

Fixed effects estimator

FMOLS:

Fully modified ordinary least square estimation

GC:

Granger causality test

GHG:

Greenhouse gas

GMM:

Generalized method of moments estimation

MG:

Mean group estimator from Pesaran and Smith (1995)

PMG:

Pooled mean group estimation

PTR:

Panel threshold regression

STIRPAT:

Stochastic impacts by regression on population, affluence, and technology model

VECM:

Vector error correction model

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Acknowledgements

The authors are thanksful to Mathilde Maurel and Guillaume Vallet for valuable feedbacks, critics, and comments. Affiliated institutions neither approve nor disapprove of the opinions expressed in this manuscript: they should be considered the authors’ own. Overall, the usual disclaimers apply.

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Authors and Affiliations

  1. The London School of Economics and Political Science, London, UK

    Nicolas Schneider

  2. Centre for Excellence in Sustainable Development, Goa Institute of Management, Sattari, India

    Avik Sinha

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NS: writing—original draft, investigation, resources, methodology, formal analysis, validation, conceptualization, writing—review and editing. AS: conceptualization, validation, review and editing.

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Correspondence to Nicolas Schneider.

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Schneider, N., Sinha, A. Better clean or efficient? Panel regressions. Climatic Change 176, 111 (2023). https://doi.org/10.1007/s10584-023-03563-8

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