The impact of renewable energy diffusion on European consumption-based emissions

† This article is a revised version of the paper that won the Wassily Leontief Memorial Prize 2015, for the best paper by authors younger than 40 submitted to the 23rd International Input–Output Conference, in Mexico City.

ABSTRACT

The amount of carbon embedded in the final consumption of goods and services in a country or region depends on the amount of goods and services consumed and the emission intensity of the production processes along global production chains. A reduction of consumption-based emissions can be achieved from both sides, a reduction in total consumption and a reduction in the emission intensity of the production processes. The power sector is one of the most carbon intensive industries along global production chains and the global deployment of renewable power generation technologies (RPGTs) is one possibility to significantly reduce emissions in this industry. This paper combines three different strands of literature, multi-regional input–output analysis, dynamic energy–economy–environment models and technological change in renewable energy (RE), to model the impact of the global diffusion of renewable energies on European consumption-based emissions. The global diffusion of RE technologies (photovoltaic and wind) depends on the development of technology costs, which are modeled using learning curves. With increasing deployment of renewables within the EU as well as increasing RD&D efforts, the EU can achieve an accelerated costs decrease for these technologies, thus fostering deployment of RPGTs at a global scale through the effect of decreasing costs. This behavior indirectly influences the electricity mix abroad, making it less carbon intensive, so that consumption-based emissions of the EU decrease.

KEYWORDS:

• Consumption-based CO₂ emissions
• renewable energy
• technology diffusion

Acknowledgements

This paper is part of a research project funded by the German Federal Ministry of Education and Research (BMBF) under the funding label Econ-C-026, whose support we gratefully acknowledge. It further draws on research conducted for the European Commission on consumption-based approaches to climate mitigation. The author is responsible for the content of this publication and further acknowledges the support by her colleagues.

Notes

† This article is a revised version of the paper that won the Wassily Leontief Memorial Prize 2015, for the best paper by authors younger than 40 submitted to the 23rd International Input–Output Conference, in Mexico City.

The examples given here are based on the author's own calculations and will be shown again in later sections of this paper.

This disregards technical problems of using only fluctuating renewable power sources.

The costs also depend on prices of the inputs into production processes, e.g. raw materials. This will be further discussed in Section 3.2.

Since we do not know anything about the future development of the countries aggregated to the region RoW, the estimates for this region presented here should be interpreted with caution.

Data for export prices at the industry/goods level (industries/goods are classified in ISIC Rev. 3. The underlying assumption for using trade data in goods and input–output data at industry level is that each industry only produces its own goods. This assumption could be refined with using supply-and-use tables) are assumed to be the same as production prices. If available, production prices are taken from the OECD STAN database. For the remaining countries historic times series for production prices at the industry level were constructed based on the general price level, wage development and energy prices, all weighted with their respective input coefficients from the IOT. The projection of the price development at the industry level uses the same approach.

Choosing the largest 1000 or more trade flows was not possible because of software limitations regarding the automated procedure for the econometric equations and their inclusion in the model.

The actual idea is that real input coefficients remain constant, while nominal input coefficients change with changing prices at the industry level. This has not yet been implemented in the model.

This analysis lacks the incorporation of projects currently planned or under construction in countries other than those covered by the OECD and IEA (2013b) data. If more countries were covered, global installations would increase even faster, thus accelerating the cost decrease and increasing the overall effects.

Note that both of these factors are influenced by changing price levels. An increasing price level enlarges the size of the final demand effect (nominal change is higher than real change), but it decreases emission intensities as CO₂ emissions are divided by a nominal output value which is higher than the real output value. In real terms, the size of the change in these factors is smaller.