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How much will it cost to mitigate climate change?

Our World in Data presents the empirical evidence on global development in entries dedicated to specific topics.
This explainer post draws on data and research discussed in our entry on CO2 and Other GHG Emissions.

United Nations (UN) member states signed the latest climate agreement in 2015; in this agreement we set an international target to limit average global warming to two degrees celcius above pre-industrial temperatures. An important question then arises: how do we achieve this, and how much will it cost to keep warming below the two degree target?

So, how do we estimate the economic cost of mitigating climate change? The most common way of measuring –and visualising– the options and costs of reducing our greenhouse gas (GHG) emissions is to use the so-called ‘abatement cost curve’ (or sometimes called ‘marginal abatement curve’). Here we will explore what these curves are and how we use them. But before proceeding, it’s important to note that these curves can be used at a range of levels to assess the best and most economic options we have for reducing GHG emissions. In particular, they can be constructed at global, regional, national or city levels. As such, a range of different abatement cost curves exist. In this post, we will be looking at a global curve developed by McKinsey&Company. Although we could have looked at a number of different examples, this analysis in particular is one of the most widely-referenced and accepted to date.

Here is a preview of what these abatement costs tell us: If we pursue aggressively all of the low-cost abatement opportunities currently available, the total global economic cost would be €200-350 billion per year by 2030. This is less than one percent of the forecasted global GDP in 2030.

What is an ‘abatement cost curve’?

An abatement cost curve measures two key variables, as shown on McKinsey&Company’s chart below: abatement potential and the cost of abatement.

‘Abatement potential’ is the term we use to describe the magnitude of potential GHG reductions which could be technologically and economically feasible to achieve. We measure this in tonnes (or thousand/million/billion tonnes) of greenhouse gases (which is abbreviated as carbon dioxide equivalents, or CO2e). Note that our measure of CO2e includes all greenhouse gases, not just CO2. So, on the x-axis we have the abatement potential of our range of options for reducing our GHG emissions; here, each bar represents a specific technology or practice. The thicker the bar, the greater its potential for reducing emissions.

On the y-axis we have the abatement cost. This measures the cost of reducing our GHG emissions by one tonne by the year 2030, and in this case is given in € (i.e. € per tonne of CO2e saved). But it’s important to clarify here what we mean by the term ‘cost’. ‘Cost’ refers to the economic impact (which can be a loss or gain) of investing in a new technology rather than continuing with ‘business-as-usual’ technologies or policies. To do this, we first have to assume a ‘baseline’ of what we expect ‘business-as-usual’ policies and investments would be. This is done –for both costs and abatement potential– based on a combination of empirical evidence, energy models, and expert opinion. This can, of course, be challenging to do; the need to make long-term predictions/projections in this case is an important disadvantage to cost-abatement curves. Whilst not perfect, it does provide useful estimates of relative costs and abatement potential.

Let’s take an example to explain these costs. The majority of the world’s private vehicles are currently diesel or gasoline-powered. Our baseline or ‘business-as-usual’ scenario in this case would be that everyone continues driving vehicles run on fossil fuels, and that growth in the car market continues in line with market projections through to 2030. One option we might consider to reduce CO2, is to begin replacing these with electric-powered vehicles. Electric vehicles (at least currently) are typically more expensive than similar gasoline-powered vehicles, so buying an electric vehicle would require a notably higher investment than ‘business-as-usual’. However, with time, the running or operating costs of an electric vehicle may be lower than a traditional car (as a result of efficiency gains and lower cost of electricity relative to liquid fuel), so we will begin to get some economic return on our initial investment. This scenario where we need an up-front investment in new technologies typically means that the capital intensity of opportunities is high, but with time the annual average cost spread across our years of operation falls.

Our potential mitigation options are ordered from left-to-right in terms of cost (getting progressively more expensive as we move to the right). The higher the bar, the greater the cost. In McKinsey&Company’s analysis, they have defined an upper cost threshold of €60 per tonne avoided (i.e. the chart only shows alternatives that are ‘cheaper’ than €60 per tonne). This is the level they consider to be most economically attractive to investors. This figure is somewhat arbitrary, and as we will see later, the report has also estimated potential emissions savings from technologies above €60 per tonne separately.

Global greenhouse gas abatement cost curve

You will see that many options to the left-hand side of the curve have negative costs. Negative costs indicate options which would actually save money. These are typically related to energy efficiency or land management projects which would provide an economic return over the longer-term.

For example, replacing conventional lighting (continuing with this form of lighting would be defined as our ‘business-as-usual’ case) with energy-efficient lighting, would more than pay back in economic terms from lower energy bills. It’s ‘cost’ is therefore negative because we would actually save money. Globally, if we took advantage of all of our negative cost options, we see that we could cumulatively avoid emissions of 11-12 billion tonnes of CO2e.

At the other end of the scale we have the more expensive options for mitigation; these are typically related to the delivery of low-carbon energy supply.

Abatement cost curves are used at a range of scales to aid investment decisions on GHG mitigation options. When making these decisions, both the abatement potential (width) and cost (height) of each option is important. For example, if we look at the lowest end of the scale we see that installing energy-efficient lighting is the cheapest option (providing a high economic return through efficiency savings). However, it’s total abatement potential is very limited. If we were looking for an investment which could save a large quantity of GHGs, we might have to select an option which is more expensive, but has a greater abatement potential. The trade-off between cost and potential is an important one.

How low could our emissions go?

If we were to implement all of our options for reducing GHG emissions, how much CO2e would we save?

On the abatement cost curve above we see that cumulatively, our options costing up to €60 per tonne avoided, have a total abatement potential of 38 billion tonnes of CO2e per year (this is the total width along the horizontal axis). This total potential has been summarised in the chart below. On the y-axis we have our global GHG emissions in billion tonnes of CO2e, and on the x-axis we see this evolution with time through to 2030.

The top line we see on this chart is our ‘business-as-usual’ (BAU) pathway. This is our future projection of how global emissions will increase if we don’t invest in technology and practices necessary to reduce our emissions; this pathway is calculated based on expected population and economic growth projections. By 2030, we expect our annual emissions to be 70 billion tonnes CO2e.

Below this we see our options for reducing emissions. Our <€60 per tonne abatement opportunities have been decomposed into energy efficiency, low-carbon energy supply and terrestrial carbon (forestry and agriculture) options. Combined, these give us our 38 billion tonnes of emissions avoided.

But we also have two additional opportunities where we could make GHG savings. The first lies in more expensive technical measures, costing  €60-100 per tonne avoided. Such opportunities are not unattainable, however, we might expect the range of cheaper options to have a significantly greater uptake before more expensive technologies are adopted. The second additional opportunity lies in social and behavioural changes which influence energy consumption and consumer choices towards lower-carbon options.

If we include these additional opportunities, our maximum technical abatement potential by 2030 totals 47 billion tonnes of CO2e per year. Our maximum global potential is therefore a 65-70% reduction relative to our current projected pathway.

Global greenhouse gas abatement potential

The total cost of global CO2 mitigation

How much would it cost in total to implement all of these options?

We can calculate this value using our abatement cost curve. The area of each of our bars represents the total cost for each technology or strategy. By adding all of these individual costs, we get the total economic cost. To get the approximated annual cost, we can divide this total by the number of years until our target date (in this case, the year 2030). This method is not strictly accurate, since it assumes that costs will be distributed equally across this timeframe; however, it gives us some relative approximation.

If we utilised all of our <€60 per tonne abatement opportunities to their full potential (which is an important assumption), McKinsey&Company estimate the total global cost to be €200-350 billion per year by 2030. This is less than one percent of the forecasted global GDP in 2030.

This cost is however, made more complex by the timing of investment. This timing component is important for financing and capital investment. Our average annual cost is calculated as a balance of how much money we need to invest, and how much we get in return based on efficiency gains and reduced running costs relative to our current technologies. However, as we noted earlier, we often need an initial capital invesment to implement the technology; this initial capital can be high, but begins to payback over time (resulting in a reduction in the average annual cost through time). If we look only at the upfront capital investment needed, this value increases to €530 billion per year by 2020 and €810 billion by 2030.


Technical notes:

It should be noted that the figures presented in cost abatement curves carry significant uncertainty and interdependencies. The total abatement cost curve is developed based on the construction and combination of individual bars related to a particular technology or practice. The width and height (i.e. abatement potential and cost) is measured independently of other technologies and variables on the curve. However, this assumption is a large and important one: many technologies (for example, solar PV and battery storage) have strong interdependencies. The evolution of one will inevitably have some impact on the other.

Technology costing is also strongly dependent on scaling effects–unit costs often decrease as capacity increases. The individual costs represented on these charts are therefore calculated with the assumption that the development and scale-up of the respective technology is approached aggressively and full technical potential is achieved. Changes in technology prices through time are assumed based on technology-specific ‘learning curves’. Learning curves measure the reduction in cost for every doubling in a technology’s capacity. For example, the learning rate for solar PV has been 22% –this means that the cost per unit of power falls by 22% for every doubling of capacity. This analysis assumes that the learning rate for specific technologies remains consistent with past trends.

McKinsey&Company stress that their estimates therefore carry a high level of uncertainty. They are useful in providing an overview outlook on the total abatement potential (and whether this would be sufficient to meet global climate targets) and estimate the potential costs of doing so. Their most useful function is perhaps in allowing for relative comparisons of costs and potential between different technologies; these factors are key for decision-makers looking to investment in low-carbon opportunities. Although imperfect, they give a relative sense of magnitude of what could potentially be achieved and if the costs can be managed to do so.

Link between health spending and life expectancy: US is an outlier

This post was originally published on August 3, 2016. It was updated on 26 May 2017.
Our World in Data presents the empirical evidence on global development in entries dedicated to specific topics.
This blog post draws on data and research discussed in our entry on how healthcare is financed.

The graph below shows the relationship between what a country spends on health per person and life expectancy in that same country between 1970 and 2015 for a number of rich countries.

The US stands out as an outlier: the US spends far more on health than any other country, yet the life expectancy of the American population is not longer, but actually shorter than in other countries that spend far less.

If we look at the time trend for each country we first notice that all countries have followed an upward trajectory – the population lives increasingly longer as health expenditure increased. But again the US stands out as the country is following a much flatter trajectory; gains in life expectancy from additional health spending in the U.S. were much smaller than in the other high-income countries, particularly since the mid-1980s.

This development led to a large inequality between the US and other rich countries: In the US health spending per capita is often more than three-times higher than in other rich countries, yet the populations of countries with much lower health spending than the US enjoy considerably longer lives. In the most extreme case we see that Americans spend more than 5-times more than Chileans, but the population of Chile actually lives longer than Americans.

[This graph and more information can be found in the entry on how healthcare is financed.]

And it is not only life expectancy

There are several aspects that contribute to the US being such an extreme outlier: Studies find that administrative costs in the health sector are higher in the US than in other countries; The price comparisons between countries rely on adjustment which are not ideally suited for comparisons of health costs and this might make comparisons more difficult. Sometimes it is also pointed out in these comparisons that violence rates in the US are higher than in other rich countries (and this is true). But while this could explain the difference in levels, it is not a likely explanation for the difference in trends. Over the period shown in the chart above violence and homicides have fallen in the US more than in other rich countries and this should have led to a narrowing of the difference to other countries and not to the increase that we see.

One of the reasons for the underachievement of the US is also the large inequality in health spending. In our entry on how healthcare is financed we discuss the empirical data on this.

The higher levels of violence in the US that kill mostly middle-aged Americans can also not explain why the US is also not just a huge outlier in total life expectancy, but also in child mortality.
The share of children that die before their fifth birthday is much higher in the US than in other rich countries even though they spend much less on healthcare.

Structural transformation: how did today’s rich countries become ‘deindustrialized’?


Most of the advanced economies of the world have been deindustrializing for decades, and they have moved into a new, ‘post-industrial’ phase of development. This post asks how they got there.

Although there are many idiosyncracies in trends, the evidence shows that as rich countries developed, the relative contribution of agriculture to both total employment and GDP declined, while the contribution of services rose in both respects. In the middle, the relative importance of the manufacturing sector first expanded and then contracted. In other words, the data shows there was first a period of industrialization as production shifted from agriculture to manufacturing, followed by a period of deindustrialization as production shifted from manufacturing to services.

In this blog post we discuss this transition and the drivers of this process of ‘structural transformation’.

The analysis in what follows is based mainly on a considerable update of the data provided by Herrendorf, Rogerson and Valentinyi (2014). You can read more about how we updated the data by consulting the ‘Sources’ tabs in the charts below.

Changes in employment

One way to study the process of ‘structural transformation’ across countries is to track how employment changed between sectors in the economy. The following chart shows this for ten of today’s rich countries. You can change the displayed country and see how very consistent the overall picture is.

In all these countries the share of workers employed in agriculture has been going down, while the share of those employed in services has been going up; and the manufacturing sector first increases and then decreases in relative importance.

By clicking on the option labeled ‘Absolute’, you can plot the total number of workers by sector. This shows a different picture, since the labor force in these countries has been growing substantially. As we can see, despite the fact that the share of employment in agriculture tends to go down constantly in all countries, in many cases the total number of workers in agriculture first increases and then decreases. The US is a clear example of this pattern: the total number of people employed in agriculture peaked more than a century ago, around 1911.

Changes in economic output

A second way to study the structural transformation is to track how output changes across sectors in the economy. The following visualization shows this, by plotting the evolution of the sectoral composition of GDP between 1800 and 2010 for the same rich countries included in the previous chart.

We can notice here the broad pattern outlined before: as rich countries developed, the shares of GDP in agriculture declined, while the shares of GDP in services went up; and in the middle, the relative importance of the manufacturing sector first expanded and then contracted. Indeed, this broad pattern is followed by all countries in the sample. (An exception is South Korea, where manufacturing has stabilized at around 40% of GDP since the early 1990s.)

This chart also shows us that in early industrializers such as the UK, Belgium, the Netherlands and France, the manufacturing sector already constituted over 30% of GDP in the first half of the nineteenth century. The USA exceeded that threshold only by the end of the century, while the other, ‘latecomer’ economies reached it at the beginning of the twentieth century. The only exception is again South Korea, which was a largely agrarian country only 50 years ago, before it began an extremely rapid industrialization process – a process that has come together with a 30-fold increase in average incomes.

When comparing the sectoral composition between countries, one must be aware that the way we measure these shares is important. The chart below shows value added in current prices, which means that the figures correspond to estimates of output value at the prevailing prices when output took place. The picture would however look different if we had chosen to show value added figures in constant prices (i.e. estimating the value of output at the prevailing prices of a fixed point in time). In the technical note at the end of this post we explain in more detail why this is. The key point to note is that in constant prices, the pattern of ‘deindustrialization’ is less marked.

The forces at play

The charts above show that there is an unambiguous trend for the agricultural share of GDP and employment to fall over the course of economic development. However, this does not necessarily mean that agricultural output falls in absolute terms.

The first chart below shows data on agriculture production in Sweden. We can see that despite the process of industrialization, agricultural output increases up to 1931, after which it first falls and then stabilizes at around 760 million constant 1910/12 Swedish Krona.

Given that the share of agriculture in total employment is decreasing throughout this period, the chart below shows us that labour productivity in agriculture is actually growing. In fact, in absolute terms it continues to grow even after agricultural output reaches its peak. This is shown in the second chart below, which plots the ratio between output and employment in agriculture.

A point that we need to keep in mind here is that this is an absolute increase in productivity. In relative terms things are different, since other sectors also increase productivity – and in fact tend to do so at a faster pace. Indeed, if we look at the charts of employment and output shares, we can see that throughout the whole period for which we have data, the share of workers in agriculture exceeds the share of agriculture in GDP. This attests to the well-documented fact that in comparison to the other sectors (i.e. in relative terms), agriculture tends to be the least productive sector in most economies.

When taken together, the evidence tells us that the story of structural transformation is not one of a stagnant agricultural sector overtaken by dynamic manufacturing and services sectors; instead, what matters are the differences in productivity growth between sectors.

Alvarez-Cuadrado and Poschke (2011) show that at early stages of development, technological improvements in manufacturing are the greatest contributors to ‘pulling’ workers out of agriculture. At later stages, the decline in the share of consumption devoted to agricultural goods, combined with continued improvements in agricultural technology, becomes the predominant force in releasing workers from agriculture, further contributing to the pattern of ‘structural transformation’.



Technical note:

The chart showing changes in the composition of GDP by sector uses current prices (i.e. figures correspond to estimates of value added at the prevailing prices when output took place). Measured this way, the changes in production values between any two years will consist of a combination of movements in prices and movements in volumes. Thus, we cannot know if the change in sectoral composition between any two years is a consequence of price changes or of more ‘fundamental’ structural changes.

An alternative way of measuring sectoral value added is by ‘chaining’ prices to those observed in a particular year, which allows us to control for changes in prices. Dividing sectoral value added by the ratio of current prices to prices in the chained year, it is possible to work out the volume of production.

Although this is a more reliable measure of the growth in productive capacity, it is difficult to use in cross-country comparisons of sectoral composition, due to the tendency of a sector to experience a decline in the price of its output as its productivity rises. Given the tendency for manufacturing to experience higher productivity growth than other sectors of the economy, and consequently for its relative price to fall over time, the implication is that the earlier the year to which we chain prices, the larger the manufacturing sector will appear to be. In fact, Rodrik (2016) finds that in large part, the continued reduction in the current price share of manufacturing in rich countries’ GDP masks the fact that when measured in real terms (ie. keeping prices constant), this share has remained fairly stable.

Global renewables are growing, but are only managing to offset the decline in nuclear production

Our World in Data presents the empirical evidence on global development in entries dedicated to specific topics.
This blog post draws on data and research discussed in our entry on Energy Production & Changing Energy Sources.

To effectively address global climate change, the world needs to transform its energy systems from a dependence on fossil fuels (which emit carbon dioxide and other greenhouse gases) to low-carbon energy sources. This process of transitioning from carbon-intensive fossil fuels to low-carbon sources is referred to as ‘decarbonisation’.

To achieve this, we have a range of low-carbon energy options. These can be defined within two main categories: renewable technologies (this is inclusive of hydropower, biomass, wind, solar, geothermal and marine sources), and nuclear energy. Both of these options produce very low CO2 emissions per unit of energy compared with fossil fuels.

If we want to reduce our CO2 emissions from energy production, we have to decrease the share of energy we produce from fossil fuels, and increase the share from renewables and/or nuclear energy. So how is the world actually performing when it comes to increasing its share of low-carbon technologies?

In the graph below we see trends in electricity production from renewable technologies (hydropower, biomass, wind, solar, geothermal and marine sources, without nuclear) and nuclear energy from 1990-2014. These figures are given as the percentage contribution to total global electricity production. In 2014, renewables accounted for approximately 22% of global electricity, and nuclear about half that at 10-11%.

What we see from 2005 onwards is a distinct divergence in renewable and nuclear trends (they are essentially a mirror image of one another). Renewable energy’s share has increased by 4-5%, meanwhile nuclear energy’s share has decreased by approximately the same (4-5%). Our share of ‘low-carbon’ electricity has remained unchanged. We have simply substituted one low-carbon energy source (renewables) for another (nuclear energy).

What we don’t produce from renewables or nuclear is, of course, produced from fossil fuels. In the chart below we have plotted the share of electricity production from fossil fuels (coal, oil and gas), and our combined low-carbon (nuclear plus renewables) sources from 1990-2014. We see that despite an increase in renewable energy production, the share of electricity production from fossil fuels has remained almost completely flat (or even increased marginally) over the last decade. It still represents 66-67% of electricity production.

Whilst the world is making progress in the uptake of renewable technologies, it appears our growing aversion to nuclear has been offsetting progress in decarbonising our electricity grids.

Carbon intensity in China’s recent history – Politics matters a lot in achieving both prosperity and sustainability

Our World in Data presents the empirical evidence on global development in entries dedicated to specific topics.
This blog post draws on data and research discussed in our entry on CO2 and other Greenhouse Gas Emissions.

The link between carbon dioxide (CO2) emissions and prosperity (GDP) has made global climate change a divisive issue to tackle. In an ideal world, we would be able to maintain development and economic growth whilst also mitigating our global CO2 emissions. This environmental-economic balance has made ‘carbon intensity’ an important metric. Carbon intensity measures the quantity of CO2 emitted per unit of GDP and is measured in kgCO2/GDP per year. A low carbon intensity indicates low CO2 emissions relative to the size of the economy. To reconcile increasing material well-being with a smaller environmental impact we need carbon intensity to fall.

Overall, we have been making progress in reducing our carbon intensity at the global level. In the chart below, we see our carbon intensity (in kgCOper int-$) from 1820-2014. Our global intensity peaked in 1951, and has since been on a gradual downward trend. This reduction in intensity has been driven by both high income and transitioning economies, with many countries across Europe and North America peaking prior to 1951 and low to middle income nations peaking later in the 20th century. We can see that since around 1980, China’s carbon intensity has declined by almost 70% through more efficient technology adoption and improved industrial practices.

We do, however, see a major disruption in China’s pathway: what happened to China’s carbon intensity over the 1950-1980 period? How did it rise to a level 4-5 times higher than the global average over only a few years?

Before we try to explain this volatility, it’s important to return to those two distinct (although often linked) variables: CO2 emissions and GDP. Looked at another way, carbon intensity is a measure of the relative difference between these two variables. For example, if a country’s GDP temporarily falls, it is possible to see an increase in intensity, even if COemissions remain the same. This is because GDP has dropped relative to CO2.

The Great Leap Forward (1958-1962)

Over the period of 1958-1962, we see a major spike in China’s carbon intensity; this coincides with the country’s ‘Great Leap Forward’ period. The Great Leap Forward was the second of China’s Five Year Plans—a series of social and economic development initiatives. But what Mao intended to be, well, a great leap forward instead marked a period of disaster, destroying all economic progress made during its first five year plan and leading to the greatest famine in modern history. Over this five-year period, China’s GDP and its population’s living standards failed to improve while its carbon emissions grew by 71%. This rapid increase in CO2 emissions relative to poor GDP growth caused the dramatic spike in carbon intensity we see in the graph above.

Why did China’s CO2 emissions grow so quickly, despite poor economic growth? Put simply, it had to do with unrealistic ambitions. Chairman Mao had a vision for China: he called for a rapid catch-up with the West in industrial production. The gap was to be closed through iron and steel production. At the time, however, China had neither the technology nor the production facilities or expertise to achieve Mao’s over-ambitious targets. Thousands of small-scale furnaces were setup across the country in response to Mao’s call for increased production. Local woods were felled to fuel the furnaces, and production was fed by scrap metal of pots, pans and furniture.

Iron and steelmaking are highly energy-intensive processes; a rapid transition from agricultural society to industrial economy alone would have been enough to drive an increase in China’s CO2 emissions. But this spike was intensified by poor technology. In an analysis of the energy use and CO2 emissions from steel production, Prince et al (2002) note that small open hearth furnaces (especially those fueled by scrap metals) can have an energy intensity five times higher than standard practices. Not only were Mao’s targets unmet, but poor technology and expertise also meant that large amounts of end-material were wasted. Feng et al (2009), who performed a detailed analysis of changes in CO2, GDP, population and energy intensity over China’s history, note that in 1958, 11 million tonnes of iron steel were produced, with 3 million discarded as unfit for use. This waste of materials, labor, and investment caused a large rise in CO2 emissions, with poor economic payback.

By 1959-60, the combination of economic downturn and a series of natural disasters drove China into, arguably, history’s most devastating famine period. Over four years, it is estimated that between 15 and 33 million people died as a result of the famine. China’s industrial and economic downfall during the 1960-1962 period caused CO2 emissions to fall, resulting in a decline in carbon intensity from its 1960 peak.

The Cultural Revolution (1966-1975)

In the few years following the Great leap Forward (1963-65), China began to recover from its famine period, with grain outputs returning to their pre-famine levels. There was also some recovery in China’s economy, with GDP increasing by 40% from the low point of the economic downturn. A slower increase in CO2 emissions of only 9% led to a small decline in its carbon intensity.

Soon after famine recovery, China’s Cultural Revolution was launched. This period was marked by comparably low GDP growth rates (less than four percent per year), and continued poverty across rural regions. Despite slow GDP growth, China’s CO2 emissions continued to rise through industrial output. This caused an increase in carbon intensity, although less intense than during the extreme episode of the Great Leap Forward campaign. The continued increase in carbon intensity finally stabilised at the end of the Cultural Revolution (1975-78), producing a second peak in China’s long-term trend.

The Economic Reform (1979-onward)

Following Mao’s death in 1976, China underwent a brief period of transition with strong GDP growth. The growth rate of CO2 emissions dropped while GDP grew 20% over the following 3-4 years, resulting in a decline in carbon intensity.

Economic reform (the decentralization of agriculture, introduction of free markets and foreign investment, and promotion of private entrepreneurship) in 1979 led to an unprecedented period of economic growth (with an annual growth rate of about 10%) and increased CO2 emissions (increasing by one billion tonnes from 1979-1990). Living standards greatly improved and the share of the Chinese population living in extreme poverty declined from 88% in 1981 to 2% today. During this period, China’s technology and industrial sector underwent rapid modernisation. This led to significant improvements in energy efficiency, productivity, and a continued decline in carbon intensity.

While we most typically associate CO2 intensity with the uptake of efficient practice or technological innovation, the complex inter-connectivity of political stability, support, economic structure, and effective national industries means that carbon intensity can sometimes show dramatic fluctuation during periods of political turbulence. Technological solutions alone are not enough—political stability and reasonable policies are also essential in achieving the twin goals of larger prosperity and smaller environmental impact.