Estimation of the relative efficiency of 20 OECD countries for mitigation of greenhouse gases

ABSTRACT

In this study, we investigated which greenhouse gas (GHG) emission sources have contributed to worsening global warming and which GHG mitigation efforts have been effective and finally evaluated the relative efficiency of OECD 20 countries in reducing total GHG and CO₂ emissions under the Kyoto Protocol regime. We applied the effect model of the stochastic frontier model. The main findings are as follows: First, it appears that the manufacturing, electricity and transportation sectors are mainly to blame for the worsening of global warming. Second, innovations in green technology are an effective method for mitigating global warming. Third, Germany is identified as the most efficient country in reducing CO₂ as well as total GHG emissions, even though Germany emits the most GHG and CO₂ among the target 20 OECD countries.

KEYWORDS:

• Greenhouse gases
• climate change
• mitigation efforts
• stochastic frontier model

JEL CLASSIFICATION:

• Q50
• Q54
• Q55

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ The effect model is not certified terminology. Lee and Shin (2014) named the SFM as ‘the effect model’.

² The total amount of GHG emissions is the sum of CO₂, methane, nitrous oxide, hydrofluorocarbons, perfluorocarbons, and sulphur hexafluoride. All GHG units are converted to tonnes in terms of CO₂ equivalent.

³ Most of GHG (CO₂, N₂O and methane) generated in manufacturing industry mainly comes from the usage of fossil fuel. Unfortunately, the data on the amount of fossil fuel used in the sector is not available, so this study uses real GDP in the manufacturing industry, which is known to have a strong positive relation with fossil fuel consumption, as a proxy variable

⁴ CO₂ is emitted during waste incineration. Garbage landfill is associated with emissions of CO₂ and CH₄.

⁵ The Cpatent_i,t is calculated by $Paten{t}_{i,t}+\sum _{j=1}^7\left(1-\tau \right)Paten{t}_{i,t-j}$, where Patent_i,t is the number of patent of country i at time t and $\tau$ is the depreciation rate. In this study, we used 12% as the depreciation rate, which was estimated by Nadiri and Prucha(1996).

⁶ If we use a balanced panel data, data for only 7 OECD countries is available. The estimation of Equation (1)(1) $\mathrm{l}\mathrm{n}{Y}_{i,t}=\alpha +\sum _{j=1}^5{\beta }_j\mathrm{l}\mathrm{n}{X}_{i,t}^j+v_{i,t}-u_{i,t}where{u}_{i,t}=z_{i,t}\delta +{\omega }_{i,t}$(1) was not converged with the balanced panel.