The real questions about the UK government’s decision to cancel the Swansea Bay Tidal Lagoon

An artist’s impression of the tidal lagoon. Image: Tidal Lagoon Power.

The UK government’s refusal to support the Swansea Bay Tidal Lagoon pathfinder project says much about how Britain proposes to face the challenges of the 21st century. Although the decision was widely expected, it still came as a severe blow to the communities in and around Swansea Bay.

But over and above the local reaction, the decision speaks volumes about the UK government’s commitment to three larger questions: mission-led innovation, rebalancing the UK economy and sustainable development.

Thanks to the popularity of Mariana Mazzucato’s work on mission-led innovation policy, the UK government has adopted this rhetoric when presenting its industrial strategy to support the technologies and industries of the future. At the heart of the new industrial policy paradigm is a joint effort between governments and business to engage in a constant dialogue to generate information about the scope for, and the barriers to, innovation. Governments play an enormously important role in catalysing new technologies and helping launch new industries by mitigating risk, an important consideration when dealing with sectors like renewable energy.

A map of the proposed project. Image: Atkins Global.

Given the need for close collaboration between government and industry, the most extraordinary aspect of the SBTL saga is that, according to Keith Clarke, the chairman of Tidal Lagoon Power, the company behind the project had heard “next to nothing” from the UK government for the past two years. So where was the partnership approach that ought to lie at the heart of mission-led innovation policy? 

The Swansea Bay Tidal Lagoon was described as a “no regrets” project by the Hendry Review that was commissioned by the same department that rejected the project last week. The Review concluded that tidal lagoons would help to deliver security of energy supply; help meet our decarbonisation commitments; and stimulate a new UK industry. The cost of a small scale pathfinder project would be about 30p per UK household per year over the first 30 years.

But the costs and the risks need to be framed over a 120 year lifespan, which makes it a totally different proposition to wind and solar (which have shorter operational lives) and nuclear (which has large waste disposal costs) – all problems that are absent from the tidal option.

The compelling vision of tidal lagoons is that the Swansea pathfinder was designed to be the first in a series of larger lagoons in which costs would certainly have decreased – as Charles Hendry suggested – through scale effects and through learning-by-doing. The UK government thus seems to have lost its ambition for mission-led innovation in the renewable energy sector.


Another policy to which the UK government is ostensibly committed is the rebalancing of the economy. This commitment was widely interpreted to mean sectoral and spatial rebalancing to render the UK less dependent on sectors like financial services and less tilted to South East England. The SBTL project was an ideal candidate to meet these twin goals because it both created a new global industry (with manufacturing located across the UK), and is located in West Wales, a “less developed region” in the EU regional classification. Creating a new industry in an old industrial region would have signalled that the UK government was genuinely committed to rebalancing the economy ahead of Brexit – but there is little evidence to suggest that such benefits were taken seriously.

Finally, the decision raises major doubts about the UK government’s commitment to sustainable development.  The Welsh Government is duty bound to take sustainability seriously because it is a requirement of the Well-being of Future Generations Act, the most innovative piece of legislation ever passed by the National Assembly for Wales. The Future Generations Commissioner, Sophie Howe, has said that the SBTL pathfinder was a perfect example of the kind of project that should be supported on sustainability grounds because of its multiple dividends in terms of environmental, social and economic benefits. What does it say about the UK’s commitment to sustainability if it is unable or unwilling to harness the power of the second biggest tidal range in the world, a power that is as predictable as it is sustainable?

The rejection of the SBTL pathfinder is also a challenge to devolved government. The Welsh Government offered to part-fund the pathfinder project to the tune of £200m to demonstrate its political commitment to the project. That commitment will now be tested like never before because it will need to ask itself how, if at all, is it possible to proceed without the support of central government.

As things stand, the rejection of the SBTL project will further embitter inter-governmental relations at a time when the level of trust between London and the devolved administrations in Scotland and Wales is already at an all-time low.      

The authors are Professors at Cardiff University.

 
 
 
 

Just like teenagers, self-driving cars need practice to really learn to drive

A self-driving car, of unknown level of education. Image: Grendelkhan/Flickr/creative commons.

What do self-driving cars and teenage drivers have in common?

Experience. Or, more accurately, a lack of experience.

Teenage drivers – novice drivers of any age, actually – begin with little knowledge of how to actually operate a car’s controls, and how to handle various quirks of the rules of the road. In North America, their first step in learning typically consists of fundamental instruction conveyed by a teacher. With classroom education, novice drivers are, in effect, programmed with knowledge of traffic laws and other basics. They then learn to operate a motor vehicle by applying that programming and progressively encountering a vast range of possibilities on actual roadways. Along the way, feedback they receive – from others in the vehicle as well as the actual experience of driving – helps them determine how best to react and function safely.

The same is true for autonomous vehicles. They are first programmed with basic knowledge. Red means stop; green means go, and so on. Then, through a form of artificial intelligence known as machine learning, self-driving autos draw from both accumulated experiences and continual feedback to detect patterns, adapt to circumstances, make decisions and improve performance.

For both humans and machines, more driving will ideally lead to better driving. And in each case, establishing mastery takes a long time. Especially as each learns to address the unique situations that are hard to anticipate without experience – a falling tree, a flash flood, a ball bouncing into the street, or some other sudden event. Testing, in both controlled and actual environments, is critical to building know-how. The more miles that driverless cars travel, the more quickly their safety improves. And improved safety performance will influence public acceptance of self-driving car deployment – an area in which I specialise.

Starting with basic skills

Experience, of course, must be built upon a foundation of rudimentary abilities – starting with vision. Meeting that essential requirement is straightforward for most humans, even those who may require the aid of glasses or contact lenses. For driverless cars, however, the ability to see is an immensely complex process involving multiple sensors and other technological elements:

  • radar, which uses radio waves to measure distances between the car and obstacles around it;
  • LIDAR, which uses laser sensors to build a 360-degree image of the car’s surroundings;
  • cameras, to detect people, lights, signs and other objects;
  • satellites, to enable GPS, global positioning systems that can pinpoint locations;
  • digital maps, which help to determine and modify routes the car will take;
  • a computer, which processes all the information, recognising objects, analysing the driving situation and determining actions based on what the car sees.

How a driverless car ‘sees’ the road.

All of these elements work together to help the car know where it is at all times, and where everything else is in relation to it. Despite the precision of these systems, however, they’re not perfect. The computer can know which pictures and sensory inputs deserve its attention, and how to correctly respond, but experience only comes from traveling a lot of miles.

The learning that is occurring by autonomous cars currently being tested on public roads feeds back into central systems that make all of a company’s cars better drivers. But even adding up all the on-road miles currently being driven by all autonomous vehicles in the U.S. doesn’t get close to the number of miles driven by humans every single day.

Dangerous after dark

Seeing at night is more challenging than during the daytime – for self-driving cars as well as for human drivers. Contrast is reduced in dark conditions, and objects – whether animate or inanimate – are more difficult to distinguish from their surroundings. In that regard, a human’s eyes and a driverless car’s cameras suffer the same impairment – unlike radar and LIDAR, which don’t need sunlight, streetlights or other lighting.

This was a factor in March in Arizona, when a pedestrian pushing her bicycle across the street at night was struck and killed by a self-driving Uber vehicle. Emergency braking, disabled at the time of the crash, was one issue. The car’s sensors were another issue, having identified the pedestrian as a vehicle first, and then as a bicycle. That’s an important distinction, because a self-driving car’s judgments and actions rely upon accurate identifications. For instance, it would expect another vehicle to move more quickly out of its path than a person walking.


Try and try again

To become better drivers, self-driving cars need not only more and better technological tools, but also something far more fundamental: practice. Just like human drivers, robot drivers won’t get better at dealing with darkness, fog and slippery road conditions without experience.

Testing on controlled roads is a first step to broad deployment of driverless vehicles on public streets. The Texas Automated Vehicle Proving Grounds Partnership, involving the Texas A&M Transportation Institute, University of Texas at Austin, and Southwest Research Institute in San Antonio, Texas, operates a group of closed-course test sites.

Self-driving cars also need to experience real-world conditions, so the Partnership includes seven urban regions in Texas where equipment can be tested on public roads. And, in a separate venture in July, self-driving startup Drive.ai began testing its own vehicles on limited routes in Frisco, north of Dallas.

These testing efforts are essential to ensuring that self-driving technologies are as foolproof as possible before their widespread introduction on public roadways. In other words, the technology needs time to learn. Think of it as driver education for driverless cars.

People learn by doing, and they learn best by doing repeatedly. Whether the pursuit involves a musical instrument, an athletic activity or operating a motor vehicle, individuals build proficiency through practice.

The ConversationSelf-driving cars, as researchers are finding, are no different from teens who need to build up experience before becoming reliably safe drivers. But at least the cars won’t have to learn every single thing for themselves – instead, they’ll talk to each other and share a pool of experience.

Johanna Zmud, Senior Research Scientist, Texas A&M Transportation Institute, Texas A&M University .

This article was originally published on The Conversation. Read the original article.