Tracking and Bending the Trends, 2014

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Bending the Trends

Who could disagree with the vision that “In 2050, around 9 billion people live well, within the limits of the planet”

Like the US Declaration of Independence, we hold certain “truths to be self-evident”, including the inalienable Right to the “pursuit of happiness” (well-being), from which everything then flows. We take global leaders at their word when they declare that the world needs to go beyond the UN Millennium Development Goals. Who could disagree with the World Business Council for Sustainable Development’s Vision 2050  that “In 2050, around 9 billion people live well, and within the limits of the planet”. Take a moment to think through the implications of not achieving that goal — especially as it seems an achievable, although enormously challenging, goal. This will involve bending trends towards sustainable ends.

“bending the trends” was coined by Jacqueline McGlade, while director of the European Environment Agency

It was the European Environment Agency, under Jacqueline McGlade (now chief scientist at the United Nations Environment Programnme), who came up with the idea of individuals and communities pledging to ‘bend the trend’ for greenhouse gas emissions in the run up to the Copenhagen Climate Change Conference, COP15, in 2011. The work of Clarity takes this further, aiming to identify and work with partners and collaborators on all the necessary trends — economic and environmental, social and governance (ESG)  — in order that WBCSD’s Vision, indeed a universal vision, can be achieved.

Tracking the Trends

In order to bend the the trends, the world needs a means to track the trends and measure performance against targets. One of the most prolific declining trends of the past decades is ice volume in the Arctic. As extraordinary in its own way, produced by Richard Wood and Kjartan Steen-Olsen at NTNU Trondheim, provides one way of tracking how nations, and individuals in those nations, are contributing to solving the challenge of perhaps 9–10 billion living well by 2050, and meeting interim targets along the way.

Above: Per Capita National impacts by territory and by personal footprint Territorial per capita impact is the amount of an activity conducted within the territorial boundaries of a country, divided by the population. Personal footprint for a country is the total impact of that activity for an average person in that country, wherever the impact occurs in the world. Here the results are arbitrarily ordered by territorial value added, and emphasise national performance against the global average: 100% is the average national impact for the world. Note also that the results are shown on a log scale. Three criteria are shown – a financial metric (value added, €), an environmental metric (greenhouse gases, kg CO2 equivalents) and a social metric (number of jobs supported). The significance of the two portrayals is one of attribution, which becomes important, for example, over the amounts of pollution for which nations take responsibility for reducing in international agreements. There are other useful ways of showing this data, for example as a percentage of how far above or below the trend countries are in order to meet 2050 targets. Source: Wood & Steen-Olsen, Ch.17 in Murray & Lenzen (2013)

The example shown in the graphs above tracks just one financial, one social and one environmental metric, and displays these for each country as percentage of the global average, but it can be applied to whatever criteria are of interest. Clarity’s True Value Accounting covers the type of information that could be tracked. The scaling factor can also be changed as desired, for example to illustrate actual amounts, and relative to unit international $ Gross Domestic Product, rather than percentages.

ESE-MRIO provides the accounting framework for concepts such as the Global Footprint, material consumption, or whatever other financial, social or environmental metrics are of interest

Their work displayed in the graph above, builds on that of many others, and compares national impacts using a technique reffered to as Socially and Environmentally Extended Multi-Regional Input Output analysis. Acronyms MRIO, EE-MRIO, SE-MRIO and ESE-MRIO could become increasingly familiar, as they gain the attention of resource accountants, people compiling global trading statistics (who increasingly now also assess the impact of trade beyond the purely monetary) and policymakers exploring trend-bending scenarios, as they quantify the flows through the complex ‘food web’ of international trade. ESE-MRIO provides the accounting framework for concepts such as the Global Footprint, material consumption, or whatever other financial, social or environmental metrics are of interest. One of its strengths is that it isn’t tied to any particular methodology, rather it can help explore the differences between them. An early example was its application to the Australian National Accounts, in Balancing Act. ESE-MRIO techniques help society highlight the critical points and priorities for trend-bending action. They also allow investors, innovators and investees to better assess where, and where not, to invest. Repeat surveys, through the changing shape of the plot, allow progress against targets to be assessed and corrective action taken.

There is a lot packed into the graphic (also see An in-depth look below). One important aspect to appreciate is the reason for showing both per-capita Territorial and Personal Footprints. Countries traditionally compile statistics, such as greenhouse gas emissions (expressed as CO2 equivalents), for the national economy, i.e. what is produced in that country—the production ‘territorial impact’. Until recently that was the only way of gathering and displaying the data. But if a country imports goods with high ‘embedded’ impact; such as greenhouse gases emitted in manufacture and transport, this isn’t shown in those ‘territorial’ statistics. But it does show up in the consumption ‘footprints’ of the individuals living in that country (see e.g. the UK or Sweden, where the solid red clay lines lie outside the dotted ones). On the other hand if, for example, a country exports a lot of fossil fuels, (see e.g. Norway or Russia) then the territorial impact may be far bigger, per person in that country, than their individual consumption footprints.

For this reason, exporting states like China, challenge the use of territorial emissions as the basis for calculating national greenhouse gas emission targets. One can speculate it is also why the Australian Federal government funded work on ESE-MRIO and were the first to produce national accounts drawn up on this basis (the Australian economy also being heavily based on the export of natural resources). But regardless of national self-interest, such countries are, we think, on the moral and practical high ground. Unless we individually and collectively can measure and attribute the true size of our footprint, we can’t take effective action. The type of actions that then become possible include personal actions, the actions of major retailers acting on the behalf of consumers (and enlightened self-interest), or through differentially reducing or removing sales tax on the least damaging routes of production (‘taxing bads, not goods’).

There is a lot of information embedded in the per capita territorial and footprint spiral. First, in order to display the huge proportional differences between countries, note that all the results are shown on a proportional scale—i.e. a ten-fold difference is shown the same, whether it is between 1 and 10% of the global average, or between 100 and 1000%. As a result of that compression, it means that what might be regarded as big absolute differences (i.e. say between 50 and 70%) can be missed at first glance. They can highlight insights that would be missed in a normal ‘arithmetic’ scale, but take some getting use to.

Second, note that the graph shows that the citizens of most countries produce, or release in their consumption, more greenhouse gases than the global per capita average of 100%. That’s because China, and India (India having the very lowest per capita impact) count as ony two countries, but hold much of the world’s population! Last, the Arabian Gulf States—massive producers and exporters of greenhouse gases—are missing from this analysis, presumably because they don’t provide compatible data.

In addition to greenhouse gases, as an indicator of environmental impact, the spiral also shows a leading social indicator, the amount of time spent working in paid employment (shown in blue as ‘jobs’) and also a leading financial indicator, national wealth (shown in gold, in terms of Euros). Unlike greenhouse gas emissions, the amount of time people spend at their jobs is remarkably uniform across the world— most countries are around 100% of the world average. On the other hand there are massive differences between the (financial) wealth generated in different countries. That doesn’t vary much between the two criteria of wealth attributed to a economic activity within a territory, or to the activities of individuals within it regardless of the source of their wealth. The authors could have ordered the countries in this radial plot by any of the six sets of data; by inspection it can be seen that this is actually done by territorial value added.

The emerging methodology underlying this is called Multi Regional Input Output Analysis (and here a particular flavour called EXIOPOL). What MRIO does in principle is, for every item bought by a consumer, trace back all the links in the chain to the original raw material, and through all of the countries, in proportion, where each step was made. Increasingly we know, for example, what the proportion are for different means of electricity generation in each country, as well as other factors that affect greenhouse gas emissions associated with different approaches to manufacturing in these different countries. Indeed we also know an increasing amount about other environmental and social impacts that can vary considerable, both in and between countries, for each manufacturing step. It is only now that the statistical data is becoming good enough to make these systematic evaluations. Even so the gaps in the data, and the different ways that the same things can be measured, means that one also requires considerable computational power both to go through all the links, and ‘solve’ for the best fit to the data given these different ways of measuring things. So while this graphic shows greenhouse gases per capita, already we can do this for much more—including water use, mineral resources, fertiliser use, land-impact, but also national indicators of social impact as well—child labour, a ‘living wage’, illness, risk of social breakdown and conflict, and others.

While this graphic, for simplicity, suggests ‘point value’ accuracy, the error ranges within EXIOPOL data are measured, and those between different EE-MIRO models are now increasingly being assessed. That different models produce different results is not in itself a problem because how things actually turn out then sheds light on which model assumptions are sound, and which are not. Generally the differences between the models are small enough to enhance the general approach, rather than discredit it.

Source: Bending the Trends, modus vivendi, 2014. MMG