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Four hurdles to getting data and science into the SDGs
  • Four hurdles to getting data and science into the SDGs

Copyright: James Morgan / Panos

Speed read

  • Measuring sustainable development is tricky and requires new data sources

  • Assigning data custodians and including non-traditional sources will be crucial

  • Targets will also need to cut across sectors and integrate science with policy

Rigorous integration will ensure the goals inspire rather than deter commitment, say Angel Hsu and Alisa Zomer.

Sustainable development is an elusive concept, one that is open to interpretation and difficult to define, let alone measure. UN negotiators therefore have a challenging task: how to specify a clear set of indicators to track the Sustainable Development Goals (SDGs) before they are finalised in September.

The SDGs are an opportunity to reach consensus on a vision for development that is science-based and applicable everywhere. But there are challenges that we — UN negotiators, donors and civil society — must collectively address to ensure the SDGs are successful and transformative.

Four of those challenges are worth highlighting for their importance in ensuring the goals are measurable in a way that sets the world on a sustainable future pathway.

Assigning ‘data custodians’

The SDGs, which replace the eight Millennium Development Goals (MDGs), were initially intended to be around five easily understandable goals. There are currently 17 draft goals and more than 100 proposed indicators. To manage and monitor their implementation will require a huge amount of data collection, analysis and management. But it is still unclear which entities will be assigned as ‘data custodians’ for each indicator, let alone individual goals.

A data custodian is a UN-designated agency or institution tasked with setting data collection criteria, definitions and guidelines. They are also in charge of monitoring and reporting on SDG progress. Custodians were designated for a few MDG goals and targets, but not universally or consistently.

Take the proposed water and sanitation SDG. It includes an indicator for wastewater treatment, but we’re nowhere close to having global data to measure it. [1] Efforts to collect country-level data to assess wastewater treatment performance revealed major problems, including inconsistent definitions of treatment. [2]

Coordinated international data collection is possible. As the custodian for water and sanitation data to assess MDG 7 (“ensure environmental sustainability”), the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply and Sanitation is considered a success story among international policymakers. [3] Although it costs millions of US dollars a year, the JMP’s method of collecting household surveys in lieu of patchy national reports shows that having a data custodian is critical to successful implementation.

It was having a single entity conducting systematic household surveys, rather than national statistics offices reporting on existing data, that made the difference.

Embracing the data revolution

The UN has called for a ‘data revolution’, but any good history student knows that revolutions rarely start at the top. They require innovation and drive from people on the ground.

Since the MDGs were negotiated in the late 1990s, advances in communications technology, low-cost sensors and even satellites have increased our capacity to collect data, measure and monitor. These new, non-traditional sources of data promise to truly revolutionise the SDGs.

For instance, Global Forest Watch (GFW), a group of government, civil society and technology partners including Google, is providing near real-time forest data collected by satellites and through on-the-ground mapping. By making the data freely available online, GFW is changing the rules of the game by putting previously expensive, proprietary data in the hands of anyone with an internet connection. This has already helped track deforestation in Madagascar and supported a transboundary treaty between Indonesia and Singapore to deal with haze pollution from forest fires.

To keep up with the technology, international bodies and countries need a scientific method for incorporating innovative data sources into the SDG monitoring and evaluation process. Who or which body will evaluate data quality from such sources, and ultimately decide whether they will be used? This is one question SDG negotiations must tackle.

Cutting across sectors

In their current form, the draft SDGs fail to adequately integrate across sectors. There are too many separate data categories and indicators, which hinder the comprehensive understanding needed for sustainable development.

Nowhere is this clearer than in the draft urban SDG. For example, the target on access to transport proposes indicators that measure proximity to public transport and frequency of service, both measured in terms of time. As drafted, the indicators fall short of addressing any of the three components of sustainable development — they overlook cost of transit (economic), pollution or climate costs (environmental) and time spent travelling (social).

This could be achieved by measuring both time and distance to different destinations including work and housing, and then setting appropriate goals for improvement. ‘Big data’ is already helping to analyse how people move through cities. [4] Such analyses could better capture transport’s urban impact on quality of life and the environment by factoring in travel costs and time, and its carbon footprint.

Linking science to policy

There has been much discussion about making the SDGs science-based. But there is often a gap between what science measures and how policies are designed.

This disconnect can be seen in how air quality is measured and communicated. An air quality index, for example, combines different pollutant concentrations into an aggregate measure to communicate air quality risks to the public — but it doesn’t link to the sources of air pollution, which is key to identifying practical steps to improve air quality. [5]

It may help policymakers more if the SDG indicators refer to pollution sources, such as transport-related emissions, than ambient air quality targets such as keeping pollution below a concentration of ten micrograms of particulate matter per cubic metre.

Creating a global sustainable development agenda requires hitting an elusive, ill-defined ‘sweet spot’ of ambition-raising combined with a realistic pathway for sustainable growth. By going beyond the health-centred scope of the MDGs, the SDGs will require new data sources. Establishing appropriate data custodians will be crucial, as will ensuring that new data are integrated with the scientific rigour needed to build a durable SDG architecture — one that inspires rather than deters political commitment.

Angel Hsu is a research scientist and lecturer at the Yale School of Forestry & Environmental Studies (FES), and director of its Environmental Performance Measurement programme. Alisa Zomer is urban research fellow at FES. Hsu can be contacted at [email protected] (@ecoAngelHsu) and Zomer at [email protected] (@azomer


[1] Omar Malik and others A global indicator of wastewater treatment to inform the Sustainable Development Goals (SDGs) (Environmental Science & Policy, April 2015)
[2] Omar Malik and others Assessing global water quality: The data challenge (Global Water Challenge, 9 December 2014)
[3] Water and sanitation (Environmental Performance Index, accessed 5 February 2015)
[4] Shalene Gupta Bright lights, big cities, bigger data (Fortune, 30 October 2014)
[5] Angel Hsu and others Toward the next generation of air quality monitoring indicators (Atmospheric Environment, December 2013)
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