The influence of energy consumption and democratic institutions on output and CO2 emissions in Bangladesh: a time–frequency approach

Abstract

This paper reports the results of a study that investigates the causal interactions among the entities energy consumption, democracy, income, and CO2 emissions in Bangladesh. Bootstrapping causality and time–frequency domain causality methods were adopted to examine the causal co-movements between the variables, using data series for a period of more than four decades. Results show that time-scale behavior plays an important role. Democracy is an important factor for emissions and national income. The nexus of democracy and CO2 emission is bidirectional. The impact of democracy on CO2 is stronger than vice versa. This study provides new insights for policymakers: democratic practices play an important role in implementing climate change policies, at least in the case of Bangladesh.

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Fig. 1

Source: All time series are collected from WDI on-line database (World Bank), except Democracy data collected from Freedom House. Sample period: 1972–2010

Fig. 2
Fig. 3

Source: Data for GDP: log of Real GDP (Gross Domestic Product) per capita, CO2: Log of Carbon dioxide emission per capita, Energy: log of energy use (KWh) per capita, are collected from WDI on-line database (World Bank), except Democracy data collected from Freedom House. Sample period: 1972–2010

Notes

  1. 1.

    Government of the People’s Republic of Bangladesh: National Adaptation Programme of Action [19].

  2. 2.

    World Development Indicator, World Bank. Summary statistics is available on request.

  3. 3.

    Bangladesh Development Initiative [2].

  4. 4.

    The application of this approach provides accurate inference in the presence of the ARCH effects, and the goal of the VAR model is to conduct ex ante inference.

  5. 5.

    The optimal lag order k is determined by minimizing an information criterion suggested by Hatemi-J [26].

  6. 6.

    Where \( \beta = vec(D) \) and vec is the column-stacking operator; the notation ⊗ represents the Kronecker product and C is a (p×n)(1 + p×n) indicator matrix with elements consisting of ones and zeros. The unrestricted VAR model, denoted by SU, is defined as \( {\mathbf{S}}_{U} = \frac{{\hat{\varepsilon }_{U}^{{\prime }} \hat{\varepsilon }_{U} }}{T - b} \), where b is the number of estimated parameters and the Wald statistic follows a χ2 distribution asymptotically with p degrees of freedom.

  7. 7.

    See Bekiros and Marcellino [3] for details.

  8. 8.

    For I(1) variables see Breitung and Candelon [5].

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Correspondence to Stelios Bekiros.

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Ahmed, A., Bekiros, S., Rosklint-Lindvall, E. et al. The influence of energy consumption and democratic institutions on output and CO2 emissions in Bangladesh: a time–frequency approach. Energy Syst 11, 195–212 (2020). https://doi.org/10.1007/s12667-018-0309-5

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Keywords

  • CO2 emissions
  • Climate policy
  • Democracy
  • Time–frequency causality

JEL codes

  • G1
  • G15