Two key elements could be the answer in the transition to net-zero: removing waste out of the electric vehicle industry, and using technological advancements to improve data.
Originally published on My CBI for the CBI Annual Conference 2020
Research Fellows at the Lloyds Banking Group Centre for Responsible Business are turning their focus to two key elements in the transition to net-zero:
• designing for reuse and removing waste out of the electric vehicle industry
• using technological advancements in AI and machine learning to improve data on the carbon accountability of business activities.
Road transport is responsible for nearly a quarter of the UK’s greenhouse gas emissions. A critical component of building back better after COVID-19 and reaching net-zero by 2050 is the widespread adoption of electric vehicles. The National Grid estimates there will be as many as 36 million electric vehicles on UK roads by 2040, but the problem of millions of used lithium-ion batteries has yet to be properly addressed.
Recently published research, based on interviews with key actors including Jaguar Land Rover, Honda, fleet operators, financiers and policymakers, found two main challenges when lithium-ion batteries pass their peak performance and no longer meet electric vehicles requirements:
1. Many of the retailers and local authorities encouraging consumers to transition to electric vehicles do not have sufficient experience or expertise in battery technologies
2. Once the capacity of lithium-ion batteries falls below 80% they become no good for electric vehicles.
However, when batteries fall below 80%, they have many possible second-life applications, significantly extending their useful life. Old electric vehicle batteries can be used as renewable energy storage in housing and in developing countries, or in the National Grid. This circular economy solution to batteries is new to key players in transforming transportation and requires co-ordination and cooperation to make it happen. At a minimum, UK manufacturers and consumers need to understand and value second-life applications of lithiumion batteries.
Many of the interviewees felt more support could be available for transition plans from petrol and diesel vehicles to electric vehicles, such as to financially incentivise the opportunities from second-life applications and make it easier to recycle lithium-ion batteries at scale. Despite sharing the aspirations of the Road to zero strategy, some local authorities feel that there is little transitional support for circular electric vehicle technologies.
To close the circle and get firmly on the road to net-zero, it is imperative for the government to recognise ‘circular economy thinking’ in policies, programmes and financially incentivise or compel the recycling or reuse 2 of lithium-ion batteries. Learning from initiatives such as the EU’s Circular Economy Action Plan, with the vision to make all products easier to repurpose and less wasteful, is essential. Billions of public money is invested in the West Midlands, including a new Battery Industrialisation Centre, which could form the hub of a large-scale advanced battery ecosystem in the UK. This low carbon industrial ecosystem could create sustainable value and address many of the burgeoning battery lifecycle challenges, such as our dependency on mainland Europe for recycling lithium-ion batteries and exploitative mining practices in developing countries.
However, focusing on the transport sector is not enough to mitigate climate change risks. How it operates alongside other sector outputs must be understood, with greater accountability as to where carbon emissions originate, to what use they are being put and how to reduce them. This creates new data collection, processing, analysis, verification and reporting challenges. Most existing systems are simply not up to task and urgently need reform. This is a big data problem where AI and machine learning can be deployed to maximum impact, increasing the scope, accuracy and usability of greenhouse gas data.
AI can identify incorrect data, detect data outliers using statistical analysis as well as use machine learning models to spot data gaps and fill in missing data. As well as using AI to seek out and connect alternative sources of data, such as traffic indices and satellite data, and infrastructure investment plans, it can also pull in data from blockchains, procurement systems and smart meters.
These sources of data are still relatively under-explored, but applying natural language processing, machine learning, text analytics, databots and sentiment analysis to different media, including greenhouse gas emissions reports, can create new insights allowing more nuanced interpretations, evaluations and knowledge of the carbon consequences of business activities. Improved carbon accountability using AI and machine learning will greatly enhance the regulation of climate change, improve the impact of investment markets and ability to coordinate collective efforts.
However, in order for these and other potential uses of AI in greenhouse gas emissions reporting to be realised, the requirements of sufficient and trusted data for the successful deployment of machine learning need to be met, as do the prerequisites of responsible and transparent AI.
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