Net zero: integrating data, digital & radical carbon reduction strategies
Updated: Feb 26
Achieving serious reductions in carbon emissions is arguably the greatest engineering challenge of the century. It requires changes to almost every aspect of society and the economy, including the technical design of energy, transport and buildings; everyday behaviours (from diets to travel); and policies – taxes, subsidies, incentives, regulations – at multiple levels from the local to the national and global.
Here I briefly set out some of the elements that could be pioneered by the most progressive cities in Europe – integrating digital and ecological strategies through a combination of new commons based infrastructures; curated data and knowledge; and active experiment and social innovation.
The challenge of implementing the Paris Agreement
Governments are struggling precisely with net zero because this is such a cross-cutting challenge, and despite the framework agreed for implementation. Ambitious targets have been set by national governments (eg net zero for Norway by 2030, Finland by 2035), and by many companies (eg Siemens, Novartis...). But few have coherent strategies for achieving the Paris agreement targets, let alone the more demanding ones needed for arresting climate change. This will become increasingly apparent in the run up to COP 26.
One crucial aspect of this is orchestration of the data, knowledge and insights needed to achieve far-reaching change in systems of energy, transport and housing, and the best ways to connect in the social economy in all its forms. So far there has been surprisingly little cross-pollination between the digital world and the world of carbon reduction – with different roles, hierarchies and cultures, whether in companies or in city administrations. This also reflects the broader problem in many cities that the people in charge of environmental issues - such as Chief Sustainability Officers or Chief Resilience Officers - are often disconnected not just from Chief Digital Officers but also from other delivery parts of city administrations. Meanwhile, very little of the data needed for cutting carbon is shared; very little follows standards; very little of what's needed is supported by contracts, regulation or procurement.
This challenge is an example of many which will arise in the next few decades where a systems approach is needed rather than simply a criteria based approach of the kind that is currently being taken around investment – ie adding in low carbon criteria to investments each of which is then treated as standalone.
For the EU’s Green Deal and national equivalents the lack of progress on data and knowledge infrastructures is fast becoming a glaring gap. There are quite well-developed plans for re-orienting investment flows away from fossil fuel industries to low carbon options (eg in the European Investment Bank). There are innumerable specific technology projects underway on everything from new battery technologies to the circular economy.
But in the implementation plans for Paris there is little serious attention to the orchestration of data and knowledge. This will matter less for actions within large companies, many of which now have their own net zero targets and plans, and well-organised supply chains. But it will matter a lot for more systemic issues, especially ones where public policy or public behaviour have a big role to play, and it matters crucially for cities which have to achieve a high proportion of the reductions.
Specifically the problem exists at several levels:
DATA how to generate, curate, share relevant data to guide action. Currently, although there are huge amounts of relevant data, relatively little of it is standardised and easily accessible – from benchmarking data within sectors to carbon emissions data of buildings, transport systems etc. Much of it is proprietary. What’s needed is the collection, curation and sharing of key data on emissions and carbon footprints of supply chains, cities and neighbourhoods, and individuals (which in turn would require new standards for data and active curation). A lot of work is underway on this – including some dashboards, projects like Carbon Tracker using satellite data to map coal emissions, the Icebreaker One project to enable investors to track the full carbon impact of their decisions, and some attempts to shift to ‘presumed open’ approaches to energy data - but it’s fairly fragmented and not integrated with money allocations.
There are individual programmes in cities such as Helsinki, Amsterdam and Copenhagen that are ambitious in scale, with Copenhagen aiming to be carbon neutral by 2025, the first capital city to do so. Yet all of their plans are detailed on buildings, transport and energy, but still very thin on data. There are some programmes designed to link data and net zero, such as the EU-funded Environmental Futures and Big Data Impact Lab but their work is still at an early stage, and mainly focused on product and service development.
There are also major unresolved issues – such as ownership and accessibility of smart meter data, and the need for new institutions to act as guardians/curators of this data. Given how much Internet of Things technologies can reveal about our lives and lifestyles, or when our homes are empty, getting this right will be vital for ensuring public trust (and avoiding the mistakes made by Sidewalk Labs and others). At a minimum this will require consistent standards. Getting a data strategy right will also be key in the long-term for shifting company reporting and the behaviour of financial markets and investors (and giving the public more reliable information on whether their pensions and other assets are either helping or hindering carbon reduction - on which the recent Harvard impact-weighted accounts provide fascinating insights). The private sector has made significant progress, pressured by investors and others wanting to track emissions throughout supply chains. The public sector has few comparable incentives.
CONTRACTS, REGULATIONS AND PROCUREMENT: a key reason for these problems is the lack of engagement with the standard powers of government. Data is often handled through ad hoc processes, or even through using markets (as Copenhagen tried). But the more efficient way to handle these issues is through making them the default, so that any private entity securing a public license (like a 5G, Uber or electricity supply license, a supermarket getting local planning permission) should be required as a condition of that license to provide relevant data in a suitably standardised, anomymised and machine-readable form. Ensuring this happens should become part of the core work of regulators.
DATA GOVERNANCE AND CITY DATA TRUSTS: I have long believed that we will need new kinds of institution to govern data in the public interest. In a paper two years ago I outlined the different forms data trusts could take, sometimes as public private partnerships acting to curate data around fields like transport and energy. The lack of such institutions is one factor behind the major problems facing smart city projects like Sidewalk Labs in Toronto and Replica in Portland, or the recent problems Singapore faced over its track and trace system. Unfortunately much of the discussion on these options has been derailed by a focus on private versions of data trusts - which are unfortunately not likely to be very useful in this context. We need instead to start experimenting with city level trusts that bring together the key players (including private sector firms in mobile, finance etc) and are charged with maximising the public value to be realised from data sharing. They should use an array of tools from contract conditions to pilots, and wherever possible focus on the outcomes to be achieved rather than fetishising the inputs (which has been the vice of too many smart city projects) .
AI AND MACHINE LEARNING: there is now a huge amount of activity underway attempting to use AI to respond to climate change, from managing electricity networks to inventing new materials. Some is focused on more detailed mapping of climate change itself and extreme weather patterns; other work on topics such as reducing energy use (eg DeepMind’s project on Google’s own energy use), transport planning, solar geo-engineering and finance. The range of this work is well captured in this recent overview. These will raise many issues including not only: effectiveness but also ownership, transparency and ethics of algorithms which will increasingly shape the life of cities. The usefulness of AI will of course depend greatly on the availability of data, and the rules governing that data.
EVIDENCE/SHARED KNOWLEDGE: the IPCC orchestrates global knowledge on the diagnosis of climate change but there is much less organised evidence about what works – in fields ranging from retrofitting to community energy to food waste. Again, market pressures mean that businesses have strong incentives to learn. But for more systemic or public interest aspects of carbon reduction there is a gap in terms of responsibility and action. Some organisations are attempting more multi-level strategies – such as Climate KIC or C40 – but their resources are limited, and C40 took a very long time to evolve into even quite modest knowledge orchestration roles (back in 2005 I worked on a proposal for them for doing this more systematically, but without success). Even where there is plenty of evaluation and evidence what’s missing is the synthesis in forms that are easily accessible, eg to a municipality or SME.
Exploratory work is underway in the UK on a ‘Net Zero’ what works centre to provide reliable guidance to frontline workers and policy makers around issues such as retrofitting homes. We now have a dozen such 'what works' centres in other fields, but surprisingly none around the environment. Like other countries the UK has a patchwork of initiatives ranging from the Zero Carbon Hub, The Green Construction Board, Active Building Centre, Centre for Research into Energy Demand Solutions (CREDS), Smart Energy Research Lab, Energy Systems Catapult (which also has a data for net zero programme), Green Finance Institute and Coalition for the Energy Efficiency of Buildings (CEEB), Mission Innovation Heating and Cooling and its associated government/UKRI funded projects – but these tend to be more supply-push rather than demand-pull, and not always designed in ways that make it easy for users to find the knowledge they need.
EXPERIMENT/INNOVATION There are large flows of funding and investment into some aspects of R&D – particularly where this fits into well-established frameworks for product innovation, but there are also major gaps, eg experiments to discover new knowledge about some of the trickier aspects of carbon reduction such as what has been learned with incentive schemes for energy efficiency, or likely job impacts of circular economies.
There will be a need for more experiment around things like home insulation, community energy, zero carbon transport, with clear hypotheses to be tested, peer learning between those running similar experiments, and rapid sharing of results (including data). Some governments – such as Finland – are putting in place more systematic methods of linking multiple local experiments around decarbonisation with shared data and learning. Such platforms for connecting experiments will be vital for Europe’s cities and towns, ideally with APIs allowing for real time consolidation of data; shared protocols for the design and assessment of experiments; and shared in-depth evidence analyses and syntheses.
ADOPTION Another crucial field for experiment – and better orchestration of knowledge - is systematic adoption of lower carbon options for production, distribution, office functions etc, particularly by SMEs, but also municipalities and NGOs. There are now some good examples: the Business Basics Fund, which Nesta helped design, is unusual in using rigorous experimental methods to find out what works in spreading adoption of new technologies and techniques. There is also good practice in places as diverse as Bavaria and South Korea; but this is currently missing from the implementation frameworks. Such adoption programmes can also be used to improve and spread new models of ownership – such as commons or cooperatively-based ownership of energy (already quite widespread in some cities) – or neighbourhood-based food clubs.
SOCIAL INNOVATION Achieving the targets will require much more success in mobilising communities to play their part in reducing emissions, learning for example from the leading ecotowns such as Freiburg. This would include topics such as reducing food waste or changing eating behaviours, again making use of data and explicit hypothesis testing about some of the options, including how to boost place-based action. Here there are useful lessons to be learned from pan-European competitions and challenges (this year’s Social Innovation Competition, for example, covers sustainable fashion).
These are all different aspects of what can broadly be termed ‘knowledge and data infrastructures’ – the intangible counterparts to the systems that find, process and move materials. They tend to be well organised within large firms but lacking across organisational boundaries and in the public sector. They range from the very hard to the very soft; from infrastructures generating data to subtle lessons being evaluated and shared.
These gaps partly reflect institutional factors. At a European level, for example, there is a strong European Investment Bank but no comparable institutions specialised in orchestrating data and knowledge. The same is true in the UK. While business has been transformed over the last 20 years with the highest capitalised businesses now being based on data and knowhow, the public sector is far behind, and some of the groups who could be playing central roles in this work – such as Chief Digital Officers in cities – are relatively uninvolved.
Europe has great strengths in digital and AI but has still not properly integrated them with thinking on climate change. It could be making the case for a greater emphasis on knowledge and data infrastructures– straddling everything from hard data from sensors to tacit knowledge of all kinds – and showing this in practice within particular sectors or cities in the context of their own targets for net zero.
COP 26 is one space for pushing this new agenda forward but we also now need a group of cities to act as pioneers and exemplars, developing data and knowledge commons for net zero which can serve as a model for the rest of Europe.