Dirty little secrets: Inferring fossil-fuel subsidies from patterns in emission intensities

Radek Stefanski 30 May 2014

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An astonishing feature of international energy and climate policy is that fossil fuels – often seen as the primary contributor to climate change – receive enormous government support (IMF 2013, IEA 2012). Surprisingly, no comprehensive database of directly measured, comparable fossil-fuel subsidies exists at the international level. This is both because of political pressure from the direct beneficiaries of subsidies and because of the immense complexity of the task given the profusion and diversity of subsidy programmes across countries (Koplow 2009, OECD 2012). Indirect measures of subsidies – such as the ones constructed by the IMF (2013) or the IEA (2012) – are based on the price-gap approach. This methodology allows researchers to infer national subsidies by comparing measured energy prices with an international benchmark price.

The key limitation of this technique is that it does not account for government actions that support carbon energy without changing its final price (Koplow 2009). Furthermore, the data necessary for this exercise is limited, and since estimates are based on energy prices measured ‘at the pump’, they incorporate significant non-traded components, which biases estimates. In Stefanski (2014), I develop a completely novel, indirect method for inferring these carbon fossil-fuel wedges by examining country-specific patterns in carbon emission-to-GDP ratios, known as emission intensities.

Approach

The method is based on two observations about carbon emission intensity. First, emission intensities follow a robust hump-shaped pattern with income, as can be seen in Figure 1(a), which plots total CO2 emissions per dollar of GDP for 26 OECD countries versus each country’s GDP per capita, for 1751–2010. Second, the emission intensity of later developers tends to follow an ‘envelope’ pattern over time. The intensities of later developers rise quickly until they roughly reach the intensity of the UK – the first country to start the modern development process – after which, their intensities tend to approximately follow the same path. An illustrative example of this envelope pattern is shown in Figure 1(b). In the graph, the obvious exceptions are China and the former USSR, which greatly overshoot this pattern. I argue that the extent to which countries like China deviate from the hump-shaped pattern is indicative of different types of distortions within those economies. Since variation in intensity can arise from numerous different sources, a model is needed to disentangle and measure these distortions.

Figure 1. Carbon dioxide emission intensity patterns

Model

In particular, I construct a simple model of industrialisation calibrated to the experience of the UK – arguably the first country to industrialise. The model reproduces the hump-shaped emission intensity by generating an endogenously changing fuel mix and energy intensity. First, the increasing part of the hump shape stems from a changing fuel mix – itself driven by the evolution of a country’s economic structure from relatively clean agriculture to relatively dirty industry and services. Second, the declining part of emission intensity stems from falling energy intensity – the energy to GDP ratio of an economy – driven by differential productivity growth and complementarity between energy and non-energy inputs.

I then examine cross-country differences in emission intensity through the lens of the calibrated model. In my framework, any deviation in a country’s emission intensity from the hump-shaped pattern (like that observed by China) is indicative of one of three distortions or ‘wedges’ within that economy:
1) A wedge on agricultural productivity;
2) A wedge on non-agricultural productivity; and, most importantly
3) A subsidy-like wedge on fossil-fuel prices.

Following the language of Chari, Kehoe, and McGratten (2007) and Duarte and Restuccia (2007), these ‘wedges’ are objects that appear like shocks to productivity or prices in a standard model, but in fact reflect a wider set of distortions, imperfections, or government policies found in the data, such as: taxation, regulation, assignment and enforcement of property rights, institutions, age of equipment, transport costs, soil and climate conditions, etc.

The contribution of the paper is to show that the envelope pattern in CO2 emission intensities is a consequence of different starting dates of industrialisation, driven by cross-country wedges in agricultural productivity. Any other deviations from the hump-shaped pattern are symptomatic of either non-agricultural productivity wedges or subsidy-like wedges on fossil fuels. Given the calibrated structural model, I can then use data on a country’s CO2 intensity, the size of its agricultural sector, and its GDP levels to infer the size of these three wedges – and in particular I can extract the subsidy wedge, which I do for 170 countries from 1980 to 2010.

Discussion

The method is immensely useful for three reasons:

First, it overcomes the problem of scarce data. The standard price-gap approach of inferring subsidies relies on comparing the price of petrol-at-the-pump across countries. This data tends to be rather scarce, whereas the necessary data for my method is far more readily available.

Second, inferring subsidies from petrol prices excludes a host of indirect subsidies that aren’t necessarily reflected in those prices like – for example – government-backed loans to energy producers (or energy-intensive firms). Since my method extracts a residual or a ‘wedge’, it provides a more comprehensive measure of support.

Finally, and most importantly, since the method is model-based, it allows me to perform counterfactuals measuring the impact of subsidies on emissions and growth.

Figure 2. Global fossil-fuel subsidies implied by the model

Results

Examining the fossil-fuel wedges obtained from the model, I find that the size of subsidies is enormous – $983 billion (1990 PPP) in 2010 alone. Wedges have also been growing – more than quadrupling since the late 1990s (see Figure 2). Crucially, I find evidence of large, indirect subsidies in some countries – including China. The model suggests that support to fossil-fuel energy in those countries is not reflected in prices of petrol at the pump, but is rather indirect. This matches well with earlier studies – such as Zhao (2001) – that find that the Chinese government supports energy-intensive industries through a wide range of indirect subsidies.

Finally, I perform a counterfactual in which I turn off energy wedges in each country and find that up to 36% of global carbon emissions between 1980 and 2010 were driven by subsidies, and that GDP was up to 1.7% lower per year because of these distortive subsidies (see Figure 3).

Figure 3. Effects of subsidies

Conclusion

Countries exhibit emission intensities that are hump-shaped with income. This column has argued that industrialisation drives this pattern whilst deviations from it are symptomatic of wedges to sectoral productivity or to the price of fossil fuels. Using a calibrated model I disentangle and measure the subsidy-like wedges on fossil fuels. The resulting subsidy-wedge data is comparable across countries and time and – unlike measures based on the price-gap approach – it also captures indirect support to fossil fuels, it controls for price differences across countries, and it is available for more countries and years.

Finally, subsidy-like wedges are expensive. Besides massively increasing carbon emissions, the total cost of fossil-fuel wedges amounted to a staggering 3.8% of global GDP in 2010 alone. To put this into the starkest possible perspective, the 2014 IPCC report estimates that climate change will lower global GDP by at most 2% per year in 50 years. By this measure, subsidy-like wedges on fossil fuels are nearly twice as damaging as climate change itself. Worryingly, these wedges are increasing over time. Whilst not all distortions can be eliminated, removing even some of these can help strained government budgets, make a significant (and cheap) contribution to the fight against climate change, and result in higher levels of global GDP.

References

Chari, V V, Patrick J Kehoe, and Ellen R McGrattan (2007), “Business Cycle Accounting”, Econometrica, 75(3): 781–836.

Duarte, M and D Restuccia (2010), “The Role of the Structural Transformation in Aggregate Productivity”, Quarterly Journal of Economics, 125(1): 129–173.

IEA (International Energy Agency) (2012), “World Energy Outlook: IEA analysis of fossil-fuel subsidies”.

IMF (International Monetary Fund) (2013), “Energy Subsidy Reform: Lessons and Implications”.

Koplow, Doug (2009), “Measuring Energy Subsidies Using the Price-Gap Approach: What does it leave out?”, International Institute for Sustainable Development, August.

OECD (2012), “Inventory of Estimated Budgetary Support and Tax Expenditures for Fossil-fuels 2013”.

Stefanski, R (2014), “Dirty Little Secrets: Inferring Fossil-Fuel Subsidies from Patterns in Emission Intensities”, OxCarre Working Paper 134.

Zhao, Jimin (2001), “Reform of China’s Energy Institutions and Policies: Historical Evolution and Current Challenges”, BCSIA Discussion Paper 2001-20, Energy Technology Innovation Project, Kennedy School of Government, Harvard University.

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Topics:  Energy Environment

Tags:  energy, emissions, pollution, subsidies, fossil fuels, energy subsidy, carbon