Drivers of Industrial and Non-Industrial Greenhouse Gas Emissions

Another new working paper this time coauthored with my masters student Luis Sanchez. We use the new approach to modeling the income-emissions relationship pioneered by Anjum et al but using total greenhouse gas emission rather than just CO2 emissions from fossil fuel combustion and cement production. This is closer to the discussion I wrote in Chapter 5 of the Working Group III IPCC Report. Anjum et al. used the more limited emissions variable because the IPCC wouldn't allow us to use the data assembled for the report in other research and it took a lot of effort on Luis' part to put the data together from the raw Edgar data. Also, economists are more familiar with the narrow industrial CO2 emissions variable and so we thought we'd do an analysis of that first.

There has been extensive analysis of the drivers of carbon dioxide emissions from fossil fuel combustion and cement production, but these only constituted  55% of global greenhouse gas (GHG) emissions (weighted by global warming potential) in 1970 and 65% in 2010. There has been much less analysis of the drivers of greenhouse gases in general and especially of emissions of greenhouse gases from agriculture, forestry, and other land uses, which we call non-industrial emissions in the paper, that constituted 24% of total emissions in 2010.

The graphs show that non-industrial emissions have a different relationship to income than do industrial emissions. However, there is still a positive relationship between the growth rates of the two variables, especially when we give more weight to larger countries as we do in the paper. Increases in the economic growth rate have about half the effect on non-industrial emissions than they have on industrial emissions.

In both of these graphs China is the large circle on the right. The country with highest non-industrial emissions is Indonesia, which is the largish circle above and to the right of China in the second graph.

We econometrically analyze the relationship between both industrial and non-industrial greenhouse gas emissions and economic growth and other potential drivers for 129 countries over the period from 1971 to 2010. As in Anjum et al., our method combines the three main approaches in the literature to investigating the evolution of emissions and income. We find that economic growth is a driver of both industrial and non-industrial emissions, though growth has twice the effect on industrial emissions. Both sources of emissions decline over time though this effect is larger for non-industrial emissions. There is also convergence in emissions intensity for both types of emissions but given these other effects there is again no evidence for an environmental Kuznets curve.

Telstra Internet

Back in 2010 I reported on the speed of the internet at home in Canberra and from my office on the ANU campus.

I just moved house and because service with iiNet was so bad and getting worse we switched to Telstra internet service. The speed is much, much higher:



The download speed is more than 8 times higher and the upload speed almost 4 times higher when accessing a server in Canberra. Accessing a server in San Jose, California:



downloading is more than 5 times faster and uploading 4 times faster.

Seminar @ Arndt-Corden 17 March

I am giving a seminar at Arndt-Corden on Tuesday 17th March at 2pm (Seminar Room B, Coombs Building, ANU) titled: "Directed Technical Change and the British Industrial Revolution". The abstract isn't entirely accurate any more - well specifically you won't see me talk about the last two sentences as we don't use a Monte Carlo analysis and we left the low elasticity of substitution for further research. We (myself, Jack Pezzey, and Yingying Lu) are close to having a paper that we are ready to put out as a working paper and submit to a journal. So, am looking forward to getting some useful comments to help us get there.

Kander et al. Paper on National Greenhouse-Gas Accounting in Nature Climate Change

Astrid Kander and coauthors at Lund and the University of New South Wales have a paper in Nature Climate Change that proposes a new way to account for embodied carbon in trade that improves on existing measures of consumption based emissions. The collaboration with UNSW was sparked when Astrid gave a presentation at Crawford School in 2012 on the topic, which was attended by Tommy Wiedmann who was then at CSIRO but moved soon after to UNSW. Astrid was visiting ANU to work on our ARC project.

The most common way to compute carbon emissions is based simply on where the emissions are produced. These are called production based emissions (PBA). It is often argued though that this approach overly penalizes countries that export emissions intensive goods and makes countries that import these goods look like their emissions are low when they benefit from emissions intensive production elsewhere. Consumption based emissions (CBA) count all the emissions produced by a country's consumption wherever in the world the goods consumed were produced. Usually, developed countries look more carbon intensive and developing countries less carbon intensive on this basis than when using production based emissions. The following Figure from Kander et al. shows that in the European Union and the USA consumption based emissions exceed production based emissions and vice-versa in China:

But if developed countries tried to produce all their imported goods at home, it is likely that their production techniques would be less emissions intensive than those in the countries that they are importing from. So, consumption based emissions accounting gives a biased view of how much developed countries have managed to reduce emissions by offshoring production. Also, if consumption based emissions were used to apportion world responsibility for reducing emissions the only strategy an importer would have to reduce emissions accounted this way is to stop importing and produce domestically which might not be economically efficient, while the exporter has no incentive to cut these emissions.

However, accounting for emissions embodied in imports based on how much carbon would be emitted if they were produced in the importing country will underestimate total global emissions and so if we want a system of apportioning emissions fairly and usefully for global climate policy purposes it is not so useful.

Kander et al.'s approach deals with the incentive issue. They measure embodied emissions in imports in the same way as conventional CBA. However, they account for exports using the world average emissions intensity for the given good to deduct emissions from exporters instead of deducting the actual emissions produced. This reduces the emissions total for exporters who produce in a low emissions intensive way and increase the emissions of emissions intensive exporters compared to CBA. These technology adjusted consumption based (TCBA) emissions do sum to world total emissions. All exporters now have an incentive to reduce their exports emissions intensity if they were held responsible for their TCBA emissions. The resulting TCBA per capita emissions are shown in the map below and the graphs above.

On this basis emissions per capita in Europe are even less than production based emissions while in the USA they are similar to consumption based emissions. Australia also doesn't look too good on the map. On the other hand, in China TCBA emissions are intermediate between CBA and PBA emissions. The strong performance of Europe is because they have lower than average emissions intensity for the products they export. The latter means that world average emissions for those products is deducted from Europe's balance but their actual emissions for producing those products is lower than that.

The biggest "winners" are Austria, Ireland, and Belgium, which look much more emissions intensive under CBA than under PBA but much less emissions intensive under TCBA.

Astrid discusses the rationale for their approach further in this news article.

Energy Prices, Growth, and the Channels in Between: Theory and Evidence

Lucas Bretschger has an interesting new paper in Resource and Energy Economics titled: "Energy prices, growth, and the channels in between: Theory and evidence". The paper argues that countries with higher energy use per capita grow slower in the long-run though reductions in energy use lower output in the short-run. The long-run effect is due to an induced increase in capital accumulation and because the model is similar to AK endogenous growth models, innovation. The paper is motivated by the stylized fact that in a sample of 37 countries, countries with higher per capita energy use grow slower. The sample includes mostly developed countries but also China and India.

This negative correlation is, however, easy to explain in terms of catch-up growth dynamics. As we show in our stylized facts paper, there is a strong positive correlation between the level of GDP per capita and the level of energy use per capita. Per capita income in countries such as China and India has risen faster than in the developed countries due to the fact that they are poorer and undergoing catch-up growth. This results in a spurious negative relationship between energy use per capita and the rate of economic growth.

This is not to say at all that Bretschger's theoretical model is wrong, but the motivation can easily be explained in another way. In fact, I'm very sympathetic to the idea that plentiful energy resources could slow the rate of economic growth as I discussed in my presentation at the AARES conference in Rotorua and my upcoming Arndt-Corden seminar on 17th March.

Bretschger also estimates an econometric model that is loosely related to his theoretical model (for example, using energy quantity rather than price due to a lack of internationally comparable data - a big problem for energy economics) that regresses the investment/output ratio on energy intensity and GDP (there are additional equations). It is not surprising that reduced energy intensity could encourage increased investment if it represents increased energy efficiency - this is one of the factors in macro-level rebound as in Harry Saunders (1992) model.

The bottom line is that the energy-output relationship is quite complicated and is probably not at all well captured by reduced form time series models. Bretschger is also making this point with his paper.