Chinese Carbon Emissions in 2023 vs. 2024

A couple of days ago I posted an update to my 2023 article on the trend in carbon emissions in China after the pandemic. That blogpost compares emissions for the whole of 2023 to those for the whole of 2019. But what happened in 2024? So far we have nine months of data, which we can compare to the first nine months of 2023.

According to Carbon Monitor, emissions have fallen by 0.6% in 2024 compared to 2023. Emissions from power generation rose by 1.6% with smaller increases in transport (0.7%) and residential (0.8%) emissions offset by a 4% fall in industrial emissions. Does this mean that Chinese emissions are peaking?

Electricity output increased by 6.3% between 2023 and 2024 so far. The increase was supplied by roughly equal increases in thermal power, hydropower, and the new renewables. The increase in hydropower is weather-related and otherwise there would have been a more significant increase in thermal power.

Coal production is only up 0.7% on last year. In the first few months of the year, coal production was lower than in the previous year but by October it was running at 6% above the level of last year.

In conclusion, the fall in emissions so far this year is probably partly due to the increase in hydroelectric output and the slow economy early in the year. This probably doesn't yet constitute a sustainable peak in emissions.

China’s Carbon Emissions Trend after the Pandemic: An Update

Last year, I published an article in The Conversation followed by a paper in Environmental Challenges with Khalid Ahmed on the trend in carbon emissions in China after the pandemic. We concluded that emissions continued to rise strongly after the pandemic. Most of the increase was in the electric power sector. A peak in emissions wasn't yet in sight. Has anything changed in the past year? 

In the published paper, we compared emissions in the first eight months of 2019 - the last year before the pandemic - with the first eight months of 2023 - the first year after the pandemic. Now we can compare data for 2019 as a whole with 2023 as whole:

The differences between 2019 and 2023 for the power sector and total emissions are a little less dramatic than those in the published paper. Emissions in the power sector increased by 18% (21% using just the first 8 months) and total emissions increased by 8% from 2019 to 2023 (10% in the published paper). The data is from Carbon Monitor. Both of these differences are extremely statistically significant. Transport emissions fell by 1% (p = 0.01). The change in residential and industry emissions between the two years are not statistically significant. The published results showed a small but statistically significant increase in industrial emissions and no statistically significant change in transport or residential emissions.

We also presented the contributions of fossil fuels, nuclear, and renewable energy to electricity generation. Here is the graph updated for all twelve months of 2019 and 2023:

The shares of solar and wind increased from 1.6 and 5.0% in 2019 to 3.2% and 9.0% in 2023 but thermal electricity generation increased by 91 TWh p.a. compared to an increased of 51 TWh from the new renewables. Nuclear and hydropower increased by a total of 6 TWh. So, though output of electricity from new renewables increased a lot, thermal power still dominated the increase in electricity generation. The share of thermal power in total generation only fell from 72.2% to 70.1%. Finally, I've updated the coal production data to the end of 2023 (exponential trend fitted):

Coal production for 2023 was 26% higher than coal production in 2019 or a compound annual growth rate of 6%.

None of these results are much different than those in our published paper. But what about 2024? That will be the subject of my next post.

My Climate Change Policy Assumptions and Expectations

Matthew Kahn posted a list of his working assumptions on climate change. I think it is really enlightening to see these laid out rather than just expressed implicitly. So I thought I'd list my ideas in response to each of Matt's points. In the following, Matt's points are in bold and mine in plain text.

1. I believe that global GHG emissions will continue to rise for decades. 

Technological change in non-carbon emitting energy technologies has been surprisingly fast despite climate policies having been relatively weak. This makes me optimistic that emissions will soon begin to fall. We used to talk about steeply rising emissions paths like RCP 8.5. In the most recent IPCC report, business as usual is now a fairly flat emissions path (not that we should put too much weight on consensus). On the other hand, I am pessimistic on energy intensity falling by as much as is assumed in many integrated assessment models (IAMs).  So, my expectation is for some fall in emissions or at least a flat path till 2050. I don't expect a steeply declining path because so much fossil fuel infrastructure continues to be built. My best guess is that we will somewhat overshoot the 2ºC target but in the later part of this century we will get really serious about carbon sequestration, which will eventually bringing the temperature down again. If we are lucky, impacts will remain fairly linear and we will avoid tipping points.

2. I do not take integrated assessment models of the impact of climate change seriously.

In general, I agree. On both the impact and technological change sides they are mostly just speculation, particularly on the impacts side. On the other hand, having some idea of how much we need to cut emissions at what cost is useful... and they can generate the social cost of carbon (see below). 

One of my standard assumptions is that technological change in terms of increasing technical efficiency of production will eventually end. It's likely that the level of technology will follow a big S shape curve from the Industrial Revolution on, and we are somewhere near the middle of the curve right now.

3. /4. Urbanization increases one’s income as one acquires more skill to succeed in the urban market. Private income growth fuels adaptation as people have more resources to protect themselves from the serious threats we now face.

Urbanization is part of the development process that increases energy use and to date carbon emissions but also provides some more adaptation capacity though it reduces other abilities to adapt. Density reduces the overall need for transport and for heating but increases the need for cooling. So overall I don't have a strong opinion on urbanization.

5. Due to market innovation, I believe that the Social Cost of Carbon (SCC) will actually decline over time.

The resource scarcity literature teaches us that the expectation that the efficient path of a price of a non-renewable resource is simply to grow at the discount rate as in the simplest Hotelling model isn't necessarily true. And if we solve the climate problem, then maybe the SCC will come back down again. I say "maybe" because though carbon in the atmosphere might be falling, we will have more to protect from impacts? In the long run, the carbon sink isn't a non-renewable resource. However, in the near term it seems reasonable to expect that the SCC is rising. Of course, the SCC is just an estimate, which is either generated by an IAM or depends on the same assumptions as an IAM. On the other hand, as long as we don't have an effective carbon price, we need a social cost of carbon number to put in cost benefit analyses.

6. The proper role of government here merits much more research. When do government efforts protect the poor versus when do government investments and rules create moral hazard and “Peltzman” effects such that we take on more risks such as moving to a risky area that the government has invested in sea walls to protect?

I think the government needs to take climate change into account when planning and adapting public infrastructure. And it has an important role in providing people information about climate change. But beyond that I don't see it has a role in adaptation. People bear the costs of adapting privately. I don't see a market failure there except due to information. So, I don't know why this should get specific attention rather than just be a side effect of general social welfare policy. Should we be building sea walls to protect land from flooding and is that a coordination problem? Well, I can't see how that can be anything but a short-term solution and so probably we shouldn't.

7. I am a fan and a producer of reduced form climate correlations. For example, over the last 4 decades how much lower has the growth rate of a nation’s per-capita income been during years when it very hot? These correlations are interesting. They play a “Paul Revere” role teaching us what future costs we could bear if we fail to adapt.

I am not a fan of this literature. I think it is more or less meaningless regarding climate change in general. If there is a one time hot year, you are not going to do long-term adaptation as Matthew points out. On the other hand, long-term impacts of climate change like sea level rise and species extinction won't happen due to one hot year. The literature can tell us something about what will happen if there are more of these exceptional years in the future but that's about it in my opinion.

China is pumping out carbon emissions as if COVID never happened. That’s bad news for the climate crisis

David Stern, Crawford School of Public Policy, Australian National University and Khalid Ahmed, Australian National University

Carbon emissions from China are growing faster now than before COVID-19 struck, data show, dashing hopes the pandemic may have put the world’s most polluting nation on a new emissions trajectory.

We compared emissions in China over the first four months of 2019 – before the pandemic – and 2023. Emissions rose 10% between the two periods, despite the pandemic and China’s faltering economic recovery. Power generation and industry are driving the increase.

Under the Paris Agreement, China has pledged to ensure carbon emissions peak by 2030 and reach net zero emissions by 2060. Our analysis suggests China may struggle to reach these ambitious goals.

Many believed the economic recovery from COVID would steer global development towards a less carbon-intensive footing. But China’s new path seems to be less sustainable than before. That’s bad news for global efforts to tackle climate change.

 

China has pledged to ensure carbon emissions peak by 2030 – but it’s heading in the opposite direction. Olivia Zhang/AP

An alarming trend in emissions

The COVID pandemic curbed greenhouse gas emissions in 2020, largely due to a drop in passenger travel. This led to hopes of a “green” economic recovery in which government stimulus spending would be invested into climate-friendly projects, to ensure a longer-term slowing of growth in emissions.

Some researchers examined the trends in China’s emissions up to 2019 and predicted the nation’s emissions would peak by 2026. Others have said the peak will occur even earlier, in 2025.

But unfortunately, it seems those predictions were too optimistic.

We examined data from Carbon Monitor, which provides science-based estimates of daily CO₂ emissions across the world. We compared emissions data from January to April 2019 (which represents typical pre-pandemic conditions in China) with the corresponding months in 2023. This period followed the removal of most COVID-related restrictions in China – such as testing requirements and quarantine rules – which essentially restored the country’s economy to business-as-usual.

We found average daily carbon emissions increased substantially between the two periods. In the first four months of 2019, China’s transport, industry, energy and residential sectors together emitted an average 28.2 million tonnes of CO₂ a day. In the first four months of 2023, daily emissions from those sectors were an average 30.9 million tonnes.

Emissions from the residential and transport sectors didn’t change much. This is mildly good news – it’s better than emissions going up. But these are the two smallest sectors, together accounting for only 18% of China’s emissions.

Rather, the increase was driven by emissions from China’s industrial and energy sectors. Average daily emissions from industry rose between 2019 and 2023 by 1.1 million tonnes or 11%. From energy, which includes electricity generation, they rose by 1.75 million tonnes or 14%.

Energy production from solar and wind in China did increase substantially between the two periods. But the growth was outweighed by electricity generated from fossil fuels.

Graph showing energy generation mix in China in the first four months of both 2019 and 2023. National Bureau of Statistics of China

Separate data show the growth of coal production in China has accelerated. In the two years prior to the pandemic, coal production variously fell or only grew slightly. But coal production grew during the pandemic, and this has continued. In the year to April 2023, coal production increased by about 5%.

While coal’s share of energy consumption fell substantially from 2007 to 2019, it has changed little since then. That’s mainly because energy use is growing fastest in the electricity sector, which remains dominated by coal.

The global picture

Emissions in many developed countries have fallen in recent years due to government policies, slow economic growth, and the shift from coal to natural gas.

Developing nations increasingly dominate global emissions. China might be expected to be a leader on the clean energy shift among developing countries – in part because it produces much less oil than it consumes. That means its energy supply is not secure, giving it an incentive to find alternative sources of power.

There’s another reason why China should be a trailblazer on emissions reduction. China is the world’s biggest emitter – so a percentage reduction in emissions there leads to far fewer tonnes of CO₂ in the atmosphere than if a smaller country reduced emissions by the same percentage. And, partly because China’s population and economy are so big, it stands to benefit more than any country in the world from a more stable global climate.

But as we’ve outlined, China’s trajectory is by no means world-leading. What’s more, moves by China on the international stage suggest it’s becoming less cooperative in climate negotiations than in recent years. We saw this at the COP27 global climate conference in Egypt late last year, when China did not join a pledge to curb methane emissions and refused to provide financial support to developing nations vulnerable to climate change.

The potential for cooperation on climate policy is being reduced further by ongoing tensions between China and the United States. All this serves to cast doubt on China following through on its Paris pledges – and certainly, on any chance its emissions will peak in the next two years.

David Stern, Professor, Crawford School of Public Policy, Australian National University and Khalid Ahmed, Visiting Fellow, Australian National University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Video of My Presentation on Asymmetric Carbon Emissions

I recently gave a seminar in Italy (virtually) on our paper on asymmetric carbon emissions over the business cycle. Here is the video:

Our COVID-19 Paper

Publishing papers on COVID-19 is very popular: 

and we couldn't resist joining the bandwagon. Late last year, Xueting Jiang, my PhD student, and I did a quick literature survey to identify a gap. Though there was a lot of research on how pollution emissions evolved over the course of the pandemic and recession, there was little putting that into the historical context of past recessions. Last year, I worked with Kate Martin, a masters student, on the relationship between carbon emissions and economic activity over the business cycle. We decided to extend that analysis. 

Our new paper uses U.S. monthly data from January 1973 to December 2020. We look at how the relationship between carbon emissions and GDP varies between recessions and expansions, but we also look at individual recessions and how emissions from different sectors vary over the business cycle. 

Like Sheldon and others, we find that, in general, the emissions-GDP elasticity is greater in recessions than in expansions, but we find that this is largely because of sharp falls in emissions associated with negative oil market shocks. The 1973-5, 1980, and 1990-1 recessions were associated with negative oil supply shocks. In 2020, there was instead a negative oil demand shock due to the pandemic. These recessions have emissions-GDP elasticities that are significantly larger than the elasticity in expansions. The elasticities in the 1981-2, 2001, and 2008-9 recessions are no larger than in expansions.

The graph shows NBER recessions in light blue stripes and nominal and real oil prices. The big spike in oil prices in 2008 came at the end of an extended increase associated with rising demand for oil. Of course, supply was constrained during this period but there wasn't a sudden supply crisis. In 1981-82 the price of oil was already falling when the recession started and it is usually regarded as having been caused by the Federal Reserve under Paul Volcker dramatically raising interest rates.

When we regress the growth of sectoral carbon emissions on the growth of national GDP, we find that the asymmetry is present in the industrial and particularly in the transport sector, which are the two largest users of oil in the US economy, using 28% and 66% of the total, respectively.

When we control for oil use, the asymmetries disappear. 

So, though the cause of the COVID-19 recession was unusual the carbon emissions outcome was similar to past recessions associated with oil crises. More importantly, we learned something new about what happens to emissions in recessions, at least in the US.

Fifth Francqui Lecture: Econometric Modeling of Climate Change

The video of my fifth and final Francqui lecture on the econometric modeling of climate change is now on Youtube:  

The lecture begins by introducing the issue of global climate change. The first image of the Earth's energy balance is from an IPCC assessment report. Probably, the 4th Assessment Report. The graph of global temperature is the Berkeley Earth combined land and sea series. The graph of CO2 concentration is based on the data we used in our Journal of Econometrics paper updated with recent observations from Hawaii. The original source of the global CO2 emissions series is the now defunct CDIAC website updated from the BP Statistical Review of World Energy. Following that are three charts from the IPCC 5th Assessment Report. World sulfur dioxide emissions are from the CEDS datasite.

The next section – "Why Econometrics" – opens with a graph of the relationship between economic growth and CO2 emissions, which I put together from World Bank, International Energy Agency, and BP data sources.

The following section – "Do GHG Emissions Cause Climate Change?" starts with original research using the temperature and CO2 time series in the previous graphs. The CO2 concentration acts as a proxy variable for all radiative forcing in this analysis. It then goes on to present results from my 2014 paper with Robert Kaufmann published in Climatic Change. Details of the data are given in that paper.

Finally, I presented my paper coauthored with Stephan Bruns and Zsuzsanna Csereklyei, which was published in the Journal of Econometrics.

Asymmetric Carbon Emissions-Output Elasticities

This semester my masters' research essay student, Kate Martin, revisited the topic of whether the carbon emissions-output elasticity is greater in recessions than in economic expansions. In other words, does a 1% increase in output increase carbon emissions by less than a 1% fall in output reduces them?

Sheldon (2017) used quarterly US GDP data and carbon emissions data from the 1950s to 2011 and found that the elasticity in recessions was much larger than in expansions when it was not significantly different to zero. There was also a strong positive drift in emissions of 5.8% p.a.

To measure output, Kate used monthly US industrial production data from 1973 to 2020 and monthly GDP data from 1992 that are available from Macroeconomic Advisers. The advantage of the longer time series is that it covers more recessions and expansions. She also compared this monthly data to quarterly data to test the effect of data frequency. She found that using industrial production and, in particular industrial CO2 emissions rather than total CO2 emissions from fossil fuels, the elasticity is actually larger in expansions but it is not statistically significantly different from the elasticity in recessions. Using GDP data at both monthly and quarterly frequencies and including the last decade of data confirmed Sheldon's basic result.

When Kate restricted the estimation period to the end of 2019, the resulting model projected emissions during the COVID recession (using the reported industrial production data) very well:

This difference between the effect of industrial production and overall GDP on emissions doesn't seem to have been commented on before. However, Eng and Wong (2017) used monthly industrial production data and found that in the short-run the elasticity is symmetric but in the long run the recession elasticity is larger.

Climate Econometrics and the Carbon Budget

Though I recently abandoned a follow up paper on our Journal of Econometrics climate modeling paper, we are now working on a different one. I'm scheduled to give a presentation (remotely) on it at the American Geophysical Union conference in December. In the course of our research, I ran some simple simulations on our Journal of Econometrics model. This model is a two equation vector autoregression of global surface temperature and radiative forcing with energy balance restrictions imposed. This is done using the concept of multicointegration. But it is still a simple time series model once the complicated estimation is complete.

I ran three scenarios that all have the same peak level of radiative forcing equivalent to doubling CO2:

Single Shock: Forcing is doubled in one year and then the system is allowed to move to equilibrium.

Shock and Maintain: Forcing is doubled suddenly and then that level of forcing is maintained forever. This is the scenario in our published paper and is used to estimate the equilibrium climate sensitivity in general circulation models.

Transient: Forcing is increased linearly for 70 years until the CO2 equivalent would be doubled. Then emissions are cut to zero.

This is what happens to temperature in the three scenarios:

Under the Shock and Maintain scenario, we reach the equilibrium climate sensitivity of 2.78ºC. Under the Transient scenario, the temperature increases by 1.85ºC when emissions are cut to zero and then continues to increase by about 0.3ºC before flatlining. Under the Single Shock scenario, temperature increases quite rapidly, reaching equilibrium in around 40 years with only a 0.98ºC increase.

This is what happens to radiative forcing in the three scenarios:

Under the Single Shock scenario there is a steep fall in forcing after the single pulse of greenhouse gases. A new equilibrium concentration and temperature is reached. Under the Transient scenario the equilibrium level of forcing is much higher even though in both cases emissions are cut to zero. Of course, much more carbon would need to be pumped into the atmosphere to achieve the Transient path as all the time carbon is also being absorbed. This shows the importance of the carbon budget. The total amount of emissions, not just the peak concentration matters. It is interesting that our very simple model seems to pick this up from the data without imposing any information about the carbon budget on the model.

Abandoning a Paper

Now and then it's time to give up on a project. In September 2018, I attended a climate econometrics conference at Frascati near Rome. For my presentation, I did some research on the performance of different econometric estimators of the equilibrium climate sensitivity (ECS) including the multicointegrating vector autoregression (MVAR) that we used in our paper in the Journal of Econometrics. The paper included estimates using historical time series observations (from 1850 to 2014), a Monte Carlo analysis, estimates using output of 16 Global Circulation Models (GCMs), and a meta-analysis of the GCM results.

The historical results, which are mostly also in the Journal of Econometrics paper, appear to show that taking energy balance into account increases the estimated climate sensitivity. By energy balance, we mean that if there is disequilibrium between radiative forcing and surface temperature the ocean must be heating or cooling. Surface temperature is in equilibrium with ocean heat, and in fact follows ocean heat much more closely than it follows radiative forcing. Not taking this into account results in omitted variables bias. Multicointegrating estimators model this flow and stock equilibirum. The residuals from a cointegrating relationship between the temperature and radiative forcing flows are accumulated into a heat stock, which in turn cointegrates with surface temperature. If we have actual observations on ocean heat content or radiative imbalances we can use them. But available time series are much shorter than those for surface temperature or radiative forcing. The results also suggested that using a longer time series increases the estimated climate sensitivity.

The Monte Carlo analysis was supposed to investigate these hypotheses more formally. I used the estimated MVAR as the model of the climate system and simulated the radiative forcing series as a random walk. I made 2000 different random walks and estimated the climate sensitivity with each of the estimators. This showed that, not surprisingly, the MVAR was an unbiased estimator. The other estimators were biased using a random walk of just 165 periods. But when I used a 1000 year series all estimators were unbiased. In other words, they were all consistent estimators of the ECS. This makes sense, because in the end equilibrium is reached between forcing and surface temperature. But it takes a long time.

Each of the GCMs I used has an estimated ECS ("reported ECS") from an experiment where carbon dioxide is suddenly increased fourfold. I was using data from a historical simulation of each GCM, which uses the estimated historical forcings over the period 1850 to 2014. A major problem in this analysis is that the modelling teams do not report the forcing that they used. This is because the global forcing that results from applying aerosols etc depends on the model and the simulation run. So, I used the same forcing series that we used to estimate our historical models. This isn't unprecedented, Marvel et al. (2018) do the same.

In general, the estimated ECS were biased down relative to the reported ECS for the GCMs, but again, the estimators that took energy balance into account seemed to do better. In an meta-analysis of the results, I compared how much the reported radiative imbalance (=ocean heat uptake roughly) from each GCM increased to how much the energy balance equation said it should increase using the reported temperature series, reported ECS, and my radiative forcing series. A regression analysis showed, that where the two matched, the estimators that took energy balance into account were unbiased, while those that did not match, under-estimated the ECS.

These results seemed pretty nice and I submitted the paper for publication. Earlier this year, I got a revise and resubmit. But when I finally got around to working on the paper post-lockdown and post-teaching things began to fall apart.

First, I came across the Forster method of estimating the radiative forcing in GCMs. This uses the energy balance equation:

where F is radiative forcing, T is surface temperature, and N is radiative imbalance. Lambda is the feedback parameter. ECS is inversely proportional to it. The deltas indicate the change since some baseline period. Then, if we know N and T, both of which are provided in GCM results, we can find F! So, I used this to get the forcing specific to each GCM. The results actually looked nicer than in the originally submitted paper. These are the results for the MVAR for 15 CMIP5 GCMs:

The rising line is a 45 degree line, which marks equality between reported and estimated ECSs. The multicointegrating estimators were still better than the other estimators. But there wasn't any systematic variation in the degree of underestimation that would allow us to use a meta-analysis to derive an adjusted estimate of the ECS.

This is still OK. But then I read and re-read more research on under-estimation of the ECS from historical observations. The recent consensus is that estimates from recent historical data will inevitably under-estimate the ECS because feedbacks change from the early stages after an increase in forcing to the latter stages as a new equilibrium is reached. The effective climate sensitivity is lower at first and greater later.

OK, even if we have to give up on estimating the long-run ECS, my estimates are estimates of the historical sensitivity. Aren't they? The problem is that I used the long-run ECS to derive the forcing from the energy balance equation. So, the forcing I derived is wrong. It is too low. I could go back to using the forcing I used previously, I guess. But now I don't believe the meta-analysis of that data is meaningful. So, I have a bunch of estimates using the wrong forcing with no way to further analyse them.

I also revisited the Monte Carlo analysis. By the way I had an on-and-off again coauthor through this research. He helped me a lot with understanding how to analyse the data. But he didn't like my overly bullish conclusions on the submitted paper and so withdrew his name from it. But he was maybe going to get back on the revised submission. He thought that the existing analysis which used an MVAR to produce the simulated data was maybe biased unfairly in favour of the MVAR. So, I came up with a new data-generating process. Instead of starting with a forcing series I would start with the heat content series. From that I would derive temperature, which needs to be in equilibrium with heat content and then using the energy balance equation derive the forcing. To model the heat content I fitted a unit root autoregressive model (stochastic trend) to the heat content reported from the Community GCM with the addition of a volcanic forcing explanatory variable. The stochastic trend represents anthropogenic forcing. The Community GCM is one of the 15 GCMs I was using and it has temperature and heat content series that look a lot like the observations. I then fitted a stationary autoregressive model for temperature with the addition of the heat content as an explanatory variable. The simulated model used normally distributed shocks with the same variance as these fitted models and volcanic shocks.

As an aside, the volcanic shocks were produced by the model:

where rangamma(0.05) are random numbers drawn from a standard gamma distribution with shape parameter 0.05. This is supposed to produce the stratospheric sulfur radiative forcing, which decays over a few years following an eruption. Here is an example realisation:

The dotted line is historical volcanic forcing and the solid line a simulated forcing. My coauthor said it looked "awesome".

So, again, I produced two sets of 2000 datasets. One with a sample size of 165 and one with a sample size of 1000. Now, even in the smaller sample, all four estimators I was testing produced essentially identical and unbiased results! I ran this yesterday. So, our Monte Carlo result disappears. I can't see anything unreasonable about this data generating process, which produces completely different results to the one in the submitted paper. So, I don't see anything to justify one over the other. So, this was the point where I gave up on this project.

My coauthor, who is based in Europe, is on vacation. Maybe he'll see a way to save it when he comes back, but I am sceptical.

Emissions Reduction Survey 2019

I again carried out a contingent valuation study of climate change using my environmental economics class as respondents. The survey was exactly as in 2018. Participants could vote yes or no on proposals to raise the Medicare levy by 0.125% or 0.25% to help fund the Emissions Reduction Fund. I designed the survey to follow the NOAA panel guidelines. I also asked the students to explain why they voted the way they did.

The results differ from 2018. Only 42% voted for a 0.125% increase in the Medicare levy, while 53% voted for a 0.25% increase. Five people voted against the smaller tax and for the larger tax. So there was quite a lot of irrational behavior where the perfect could have been the enemy of the good if one person had voted differently on the higher tax. This kind of thinking is in large part, IMO, why Australia doesn't now have a carbon price...

Of those voting no on both proposals, there were a mix of responses. Only one seemed to be saying that they couldn't afford the tax given the benefit! And that is what such a survey is supposed to measure. Others objected to the payment vehicle, by suggesting that the government should price carbon or reduce the diesel rebate etc. or borrow/print money instead. I agree with the first two of these, but again that leads here to nothing happening on the climate front if that is what you care about. Others worried about the distributional impact. That is a valid criticism of the Medicare levy proposal, which is a tax on all ones income rather than a progressive or marginal tax. One person incorrectly thought the Medicare levy was unethical, as it was a tax on healthcare. Actually, it is just an extra income tax.

Of those voting yes to the lower tax and no to the higher tax, only one mentioned the cost. The others said that the government should find other funding (borrowing?) or polluters should pay – of course in the end it is the consumer who will pay to the degree that polluters can pass on costs…

Those voting yes on both proposals all said the tax increase was affordable, so they did consider actual willingness/ability to pay.

The bottom line, is that there is a lot of behavior going on in the responses to this survey which doesn’t fit with the model of paying for a public good model where people state their honest WTP, even with a supposedly state of the art design. There is some free-riding - other people should pay or the government should borrow – and on the other hand some altruism as well as protest votes about the policy design. There is also irrational behavior represented by voting no, yes, though we probably can assume that some of these didn't understand the potential implication of voting against the lower tax rate.

A Multicointegration Model of Global Climate Change

We have a new working paper out on time series econometric modeling of global climate change. We use a multicointegration model to estimate the relationship between radiative forcing and global surface temperature since 1850. We estimate that the equilibrium climate sensitivity to doubling CO2 is 2.8ºC – which is close to the consensus in the climate science community – with a “likely” range from 2.1ºC to 3.5ºC.* This is remarkably close to the recently published estimate of Cox et al. (2018).

Our paper builds on my previous research on this topic. Together with Robert Kaufmann, I pioneered the application of econometric methods to climate science – Richard Tol was another early researcher in this field. Though we managed to publish a paper in Nature early on (Kaufmann and Stern, 1997), I became discouraged by the resistance we faced from the climate science community. But now our work has been cited in the IPCC 5th Assessment Report and recently there is also a lot of interest in the topic among econometricians. This has encouraged me to get involved in this research again.

We wrote the first draft of this paper for a conference in Aarhus, Denmark on the econometrics of climate change in late 2016 and hope it will be included in a special issue of the Journal of Econometrics based on papers from the conference. I posted some of our literature review on this blog back in 2016.

Multicointegration models, first introduced by Granger and Lee (1989), are designed to model long-run equilibrium relationships between non-stationary variables where there is a second equilibrium relationship between accumulated deviations from the first relationship and one or more of the original variables. Such a relationship is typically found for flow and stock variables. For example, Granger and Lee (1989) examine production, sales, and inventory in manufacturing, Engsted and Haldrup (1999) housing starts and unfinished stock, Siliverstovs (2006) consumption and wealth, and Berenguer-Rico and Carrion-i-Silvestre (2011) government deficits and debt. Multicointegration models allow for slower adjustment to long-run equilibrium than do typical econometric time series models because of the buffering effect of the stock variable.

In our model there is a long-run equilibrium between radiative forcing, f, and surface temperature, s:

The equilibrium climate sensitivity is given by 5.35*ln(2)/lambda. But because of the buffering effect of the ocean, surface temperature takes a long time to reach equilibrium. The deviations from equilibrium, q, represent a flow of heat from the surface to (mostly) the ocean. The accumulated flows are the stock of heat in the Earth system, Q. Surface temperature also tends towards equilibrium with this stock of heat:

where u is a (serially correlated but stationary) random error. Granger and Lee simply embedded both these long-run relations in a vector autoregressive (VAR) time series model for s and f. A somewhat more recent and much more powerful approach (e.g. Engsted and Haldrup, 1999) notes that:

where F is accumulated f and S is accumulated s. In other words, S(2) = s(1)+s(2), S(3) = s(1)+s(2)+s(3) etc. This means that we can estimate a model that takes into account the accumulation of heat in the ocean without using any actual data on ocean heat content (OHC) ! One reason that this is exciting, is because OHC data is only available since 1940 and data for the early decades is very uncertain. Only since 2000 is there a good measurement network in place. This means that we can use temperature and forcing data back to 1850 to estimate the heat content. Another reason that this is exciting is that F and S are so-called second order integrated variables (I(2)) and estimation with I(2) variables, though complicated, is super-super consistent – it is easier to get an accurate estimate of a parameter despite noise and measurement error issues in a relatively small sample. The I(2) approach combines the 2nd and 3rd equations above into a single relationship which it embeds in a VAR model that we estimate using Johansen's maximum likelihood method. The CATS package which runs on top of RATS can estimate such models as can the Oxmetrics econometrics suite. The data we used in the paper is available here.

This graph compares our estimate of OHC (our preferred estimate is the partial efficacy estimate) with an estimate from an energy balance model (Marvel et al., 2016) and observations of ocean heat content (Cheng et al, 2017):

 

We think that the results are quite good, given that we didn't use any data on OHC to estimate it and that the observed OHC is very uncertain in the early decades. In fact, our estimate cointegrates with these observations and the estimated coefficient is close to what is expected from theory. The next graph shows the energy balance:

The red area is radiative forcing relative to the base year. This is now more than 2.5 watts per square meter – doubling CO2 is equivalent to a 3.7 watt per square meter increase. The grey line is surface temperature. The difference between the top of the red area and the grey line is the disequilibrium between surface temperature and radiative forcing according to the model. This is now between 1 and 1.5 watts per square meter and implies that, if radiative forcing was held constant from now on, that temperature would increase by around 1ºC to reach equilibrium.** This gap is exactly balanced by the blue area, which is heat uptake. As you can see, heat uptake kept surface temperature fairly constant during the last decade and a half despite increasing forcing. It's also interesting to see what happens during large volcanic eruptions such as Krakatoa in 1883. Heat leaves the ocean, largely, but not entirely, offsetting the fall in radiative forcing due to the eruption. This means that though the impact of large volcanic eruptions on radiative forcing is short-lived, as the stratospheric sulfates emitted are removed after 2 to 3 years, they have much longer-lasting effects on the climate as shown by the long period of depressed heat content after the Krakatoa eruption in the previous graph.

We also compare the multicointegration model to more conventional (I(1)) VAR models. In the following graph, Models I and II are multicointegration models and Models IV to VI are I(1) VAR models. Model IV actually includes observed ocean heat content as one of its variables, but Models V and VI just include surface temperature and forcing. The graph shows the temperature response to permanent doubling of radiative forcing:

The multicointegration models have both a higher climate sensitivity and respond more slowly due to the buffering effect. This mimics, to some degree, the response of a general circulation model. The performance of Model IV is actually worse than the bivariate I(1) VARs. This is because it uses a much shorter sample period than Models V and VI. In simulations that are not reported in the paper, we found that a simple bivariate I(1) VAR estimates the climate sensitivity correctly if the time series is sufficiently long - much longer than the 165 years of annual observations that we have. This means that ignoring the ocean doesn't strictly result in omitted variables bias as I previously claimed. Estimates are biased in a small sample, but not in a sufficiently large sample. That is probably going to be another paper :)

* "Likely" is the IPCC term for a 66% confidence interval. This confidence interval is computed using the delta method and is a little different to the one reported in the paper.
** This is called committed warming. But, if emissions were actually reduced to zero, it's expected that forcing would decline and that the decline in forcing would about balance the increase in temperature towards equilibrium.

References

Berenguer-Rico, V., Carrion-i-Silvestre, J. L., 2011. Regime shifts in stock-flow I(2)-I(1) systems: The case of US fiscal sustainability. Journal of Applied Econometrics 26, 298—321.

Cheng L., Trenberth, K. E., Fasullo, J., Boyer, T., Abraham, J., Zhu, J., 2017. Improved estimates of ocean heat content from 1960 to 2015. Science Advances 3(3), e1601545.

Cox, P. M., Huntingford, C., Williamson, M. S., 2018. Emergent constraint on equilibrium climate sensitivity from global temperature variability. Nature 553, 319–322.

Engsted, T. Haldrup, N., 1999. Multicointegration in stock-flow models. Oxford Bulletin of Economics and Statistics 61, 237—254.

Granger, C. W. J., Lee, T. H., 1989. Investigation of production, sales and inventory relationships using multicointegration and non-symmetric error correction models. Journal of Applied Econometrics 4, S145—S159.

Kaufmann R. K. and D. I. Stern (1997) Evidence for human influence on climate from hemispheric temperature relations, Nature 388, 39-44.

Marvel, K., Schmidt, G. A., Miller, R. L., Nazarenko, L., 2016. Implications for climate sensitivity from the response to individual forcings. Nature Climate Change 6(4), 386—389.

Siliverstovs, B., 2006. Multicointegration in US consumption data. Applied Economics 38(7), 819–833.

More on Millar et al.

Millar and Allen have an article in the Guardian explaining what their paper really says. They say that existing ESMs assume too little cumulative emissions by the 2020's when atmospheric carbon dioxide will be higher than now and so temperature higher than now. But we have already reached that level of cumulative emissions and so we need to do an adjustment to the graph of cumulative emissions vs temperature. But no change to the graph of temperature vs. current concentration of CO2. The discrepancy arises because of uncertainty in cumulative emissions. Models have backfilled this estimate from other variables. Then they say that human induced warming is 0.93C and so that is the temperature baseline of their shifted frame of reference:

The argument of critics like Gavin Schmidt, Zeke Hausfather, and me that the estimate of current human-induced warming is too low still stands. And this means that the remaining carbon budget is smaller than argued by Millar et al. Any likely path to 1.5 degrees will require exceeding that temperature and then bringing radiative forcing down again.

Is the Carbon Budget for 1.5 Degrees Much Larger than We Thought?

An article in Nature Geoscience by Millar et al. on carbon budgets has attracted a lot of attention and debate.* A blogpost by the lead author explains that current human-induced warming has increased in the 2010's by 0.93C over the the 1861-1880 period, while the mean CMIP5 climate model run projected that given cumulative carbon emissions to date the temperature should be 0.3C warmer than that. They argue that that means that the remaining carbon emissions budget allowed for staying within a 1.5C increase in temperature is larger than previously thought as we have 0.6C to go at this point rather than 0.3C. I think there a number of issues with this claim.

First, the value for human-induced warming is based on averaging the orange line in this graph:

The orange line is derived by fitting estimated radiative forcing to observed temperature given by the HADCRUT4 dataset by regression. HADCRUT4 shows less increase in surface temperature than either the GISS or Berkeley Earth datasets because of how it covers the polar regions, in particular.
Using the Berkeley Earth dataset, the temperature increase from the 1861-80 mean to the 2010's mean – shown by black lines in this graph:

is 1.1C. As you can see, even that increase is assuming that conditions during the "hiatus" are more usual than those during the post-2014 increase in temperature. 0.93C is a very conservative estimate of warming to date. Though the recent period was affected by El Nino conditions, it's possible that it represents catching up to the long term trend rather than an excursion above the trend. Throughout the hiatus period ocean heat content was increasing. I do think it is likely that the jump in temperature in the last two years is a recoupling of surface temperature to this more fundamental trend. We have a paper under review that supports this view.**

Also, I think that averaging the orange trend line in the previous graph definitely is too conservative given the strongly non-stationary behavior of the trend. The most recent estimate of the trend would be a better guess.

Second, I think there are a few reasons*** why we might update the carbon budget (as measured from the beginning of the Industrial Revolution):

1. Our estimate of the transient climate sensitivity changes – we think that the short-run temperature for a given concentration of carbon dioxide in the atmosphere is higher or lower than we previously thought.

2. Our estimate of the airborne fraction changes – our estimate of the amount by which the carbon dioxide in the atmosphere increases in reaction to a given amount of emissions changes. CO2 in the atmosphere has increased by about half cumulative emissions.

3. Our estimate of non-CO2 forcing changes. There are important other sources of radiative forcing such as methane and sources of negative forcing such as sulfate aerosols.

Observations of warming to date, isn't one of these. So the paper is implicitly saying that these observations lead them to reduce their estimate of the climate sensitivity.

Third, though the paper says that Earth System Models overestimated warming to date, it seems that the authors use the same models to estimate the remaining carbon budget.

* I have extensively revised this post following a comment from Myles Allen, one of the paper's authors. Also, I realized that the second part of the post didn't really make much sense, so I deleted it.

** The paper has been in review since February, but we haven't posted a working paper, as one of my coauthors didn't want to do so before receiving referee comments.

*** The emissions path also affects the carbon budget as we can see from the mean values for the various RCP paths in the graph below from Millar et al. and the difference between the red plume of RCP paths and the grey plume which are paths where emissions grow at a constant 1% per annum rate. The slower we release carbon, the bigger the budget.

Interview with Western Cycles Blog

Alejandro Puerto is a 20 year old who lives in Cuba. He has written: "Western Cycles: United Kingdom" a book that covers the economic and political history of the UK from 1945 onwards. He maintains a website of the same name that showcases his writing. You can also follow him on Twitter. He asked me whether I would I would do an interview for his blog. Here it is:

When did you became interested in the energy and the environment on economics?

I was interested in the environment from an early age and so I studied geography, biology (and chemistry) in the last 2 years of high school in England (1981-3) and then went on to study geography at university (in Israel). I had to pick another field and initially chose business as something practical but quickly switched to economics. I then realised that economics could explain a lot of geography and environmental trends. It was only when I went to do my PhD starting in 1990 that the faculty at Boston University at the Center for Energy and Environmental Studies which was linked to the Geography Department there were really focused on the role of energy in the economy and environmental trends that I became interested in understanding the role of energy. So I got a PhD in geography officially but had quite a lot of economics training and over time drifted closer to economics, so now I am even director of the economics program at the Crawford School of Public Policy at ANU.

I think that my generation is more informed on climate change because of the work of people like you. Do you think the same? Describe us some of your research.

Well, I think it has just become a much bigger and obvious issue as the global temperature has increased. The awareness of what is happening has been driven by people in the natural sciences. I have done some research applying time series models used in macroeconomics to modelling the climate system and though our first paper was published in Nature in 1997 and we have been cited on that in IPCC reports it has largely been on the fringes of climate science. My view of that research is that it takes an entirely different approach to modelling the system than most climate scientists use (mostly they use big simulation models called GCMs) and finds similar results which strengthens their conclusions. Most of my research has been on the role of energy in economic growth and the effect of economic growth on emissions and concentrations of pollutants. The effect of energy on growth is much more complicated than many people think – it seems that energy is more important as growth driver in the past in the developed world – adding energy when you have little has more effect than when you already have a lot. On pollution I’ve argued that the idea of the environmental Kuznets curve – that as countries get richer eventually growth will actually be good for the environment and reduce pollution is either outright wrong or too simplified. Instead in fast growing countries like China, growth overwhelms efforts to reduce pollution, while in slower growing developed economies clean up can happen faster than growth.

The Paris Summit filled your expectations as an environmental economist?

It was probably better than expected give the lack of success in getting agreement before then. Countries pledges are too little to reach the goal of limiting warming to 2C and we will probably have to remove carbon from the atmosphere in a big way later in this Century. The real question is whether countries will actually fulfil their voluntary pledges. OTOH low-carbon technology is developing fast and that is a positive that is making achieving the goals looking more possible.

How dangerous would be the environmental policy of the United States under the Trump administration on climate change?

It will delay action, unclear how much effect it will really have. Encouraging the development of new technology is important and having the largest and leading economy not focused on that is a negative. The US can’t actually leave till late 2020 and Trump has left the door open to submitting a weaker INDC in the interim and claiming victory. The US will still be involved in UNFCCC talks etc.

What do you think about the emissions of developing countries as they become industrious?

Developing country emissions are now larger than developed country emissions. But there is a big difference between China which now has higher per capita emissions than the European Union and say India which has still very low per capita emissions. China needs to take action and has made a moderately strong pledge. We should expect much less from India say. India is, though, strongly encouraging renewables development. Hopefully, technology is advancing fast enough that the poorest countries will end up going down a lower carbon path anyway as fossil fuel technologies gradually phase out.

Since 2006 China has become the greatest global polluter and emissions still growing continuously. China has no plans for decrease these emissions until 2030. What do you think about the attitude of this country?

They say they will peak emissions by 2030. In terms of reduction in emissions intensity per dollar of GDP their goal is quite strong. In the last 3 years Chinese CO2 emissions have been constant. Some argue they are already peaking now. I am a bit more skeptical. We need to see a few more years. There are several reasons why China is pursuing a fairly strong climate policy including energy security, encouraging innovation and reducing local air pollution as well as realising that they can benefit a lot from reducing their own emissions because they are such a large part of the problem.

In the long term, which kind of renewable energy would be the first to think about? Solar? Wind?

Solar – it has a greater potential total resource and looks like eventually prices will be below wind. Wind of course is strong in places without much sunshine like the Atlantic Ocean off NW Europe. I’m concerned though about the environmental impact of lots of wind power. In the long-run I’m still hoping for fusion to work out :)

Tell us about one of your favorite posts published by you on Stochastic Trend.

I’ve done less blogging recently as I now use Twitter for short things. Most of the posts are excerpts from papers or discussions of new papers. The most popular blogpost this year with visitors is:

http://stochastictrend.blogspot.com.au/2017/03/from-wood-to-coal-directed-technical.html

Where I discuss our working paper on the role of coal in the Industrial Revolution. The research and writing of this paper took a very long time and I was really happy to be able to announce to the world that it was ready.

Do you drive an electric car?

No, I don’t have a driving licence. My wife drives and we have a car but it is a large petrol-engined car that is not very efficient. We don’t drive it much though. We’ve driven less than 30,000 km since buying it in 2007.

Have you ever visited Cuba? Are you interested? There are a lot of 1950s cars, but there are places with tropical nature.

No, I haven’t been to Cuba. The only place I’ve been in Latin America is Tijuana, Mexico. I’m not travelling that much recently as we now have a 1 1/2 year old child. But Cuba probably wouldn’t be high on my agenda. I travel mostly to either visit family or go to academic conferences and work with other researchers. The only time I flew somewhere outside the country I was living in just to go on vacation was when I flew from Ethiopia to Kenya. I was at an IPCC meeting in Ethiopia.

How Accurate are Projections of Energy Intensity?

A new short working paper about how accurate projections of future energy intensity are. It's an extension of comments I made at Energy Update 2016 here at the ANU.

Energy intensity is one of the four factors in the Kaya Identity, which is often used to understand changes in greenhouse gas emissions. It is one of the two most important factors together with the rate of economic growth. The 2014 IPCC Assessment Report shows that less than 5% of models included in the assessment project that energy intensity will decline slower than the historic rate under business as usual:*

Is this likely? In the paper, I evaluate the past performance of the projections implied by the World Energy Outlook (WEO) published annually (except in 1997) by the International Energy Agency (IEA). The following graph shows the average annual difference between the projected and actual rate of change in energy intensity in subsequent years** for each WEO since 1994:

Positive errors mean that energy intensity declined slower than projected in the following years while negative errors mean it declined faster. So, for example, the error of -0.4% for 2000 means that over the years 2001-2015, on average energy intensity declined by 0.4% a year faster than was projected in the 2000 WEO.

It turns out that these errors are strongly negatively correlated (r = -0.8) with the error in projecting the rate of economic growth, which IEA outsources. Csereklyei et al. (2016), similarly, find that reductions in energy intensity tend to only occur in countries with growing economies. If we divide and multiply the growth rate of energy intensity g(E/Y) by the growth rate of GDP g(Y) we get the following identity:

The first term on the right hand side can be seen as the elasticity of energy intensity with respect to GDP.*** The following graph plots the elasticity as projected and as subsequently realized for each WEO:

The two seem to have tracked each other quite well. But there is a complication. The 1994 to 96 WEOs only projected future energy use up to 2010. 2010 is the only recent year when global energy intensity actually increased. This end point reduces (in absolute value) the actual elasticities for these three WEOs. From 1998 on, the difference between the projected and actual rate of change in energy intensity is calculated up to 2015. But through the 2011 WEO, 2010 is one of the years in the projection period. From 2012, 2010 is no longer include in the projection period and there is a sharp step down in the actual elasticity over the projection period. I think that the elasticities for 2012-16 probably under-estimate the true long-run elasticities and that the relatively stable values from 1998-2011 are more representative of what the future elasticities will be over the full projection horizon to 2030 or 2040.

If that is the case, then the projected elasticity of -0.6 in the 2016 WEO probably over-estimates the the elasticity that will be realized in the long run. Why would this be the case?

Early WEOs largely modeled energy intensity trends based on historical trends. This is not the case for recent WEOs. Over time, the IEA has endogenized more variables in their model of the world energy system and included more and more explicit energy policies. It is likely that the model under-estimates the economy-wide rebound effect. It's also possible that energy efficiency policies are not implemented as effectively as expected.

As part of our ARC funded DP16 project, we hope to contribute to improving future projections of energy intensity by empirically estimating the economy-wide rebound effect.

* The light grey area indicates the projections between the 95th and 100th percentile of the range for the default scenario.

** The base year for each WEO is 2-3 years before the publication date. Therefore, we can already assess the 2015 and 2016 WEO's.

*** We can use the identity to decompose the projection errors:

Over time the contribution of errors in the projected growth rate has increased relative to the contribution from errors in the elasticity. But I think that if we revisit this experiment in 2030 we will find a larger contribution from errors in the elasticity for what are currently recent issues of the WEO.

P.S. 23 June 2017

The paper is now published in Climatic Change.