A reform of the Green Belt is long overdue. Here’s how it should be used

A waste of space. Image: London First/Quod/SERC.

The Metropolitan Green Belt is embedded in people’s psyche as the epitome of British countryside alongside the moors of Yorkshire and the wilderness of the Scottish Highlands. It embodies a romantic vision of a preserved landscape – a green and pleasant land where time stays still in contrast to the dizzying urban beast that is London, spewing out pollution, noise and decadence.

Whilst many areas within the Metropolitan Green Belt are undoubtedly beautiful and should be vehemently preserved and nurtured, there is mounting pressure to release more of the designated land to ease housing pressures. This unsurprisingly pits conservationists against developers, idealists against pragmatists, and feeds into party politics.

Much of the debate revolves around housing development and whether increased development will signal the death of the Green Belt as a policy concept; a reductionist and a narrow prism through which to see the debate. A wider strategic view of Green Belt policy is required to address the multiple and interconnected issues facing London and the South-East.

The idyll of the Green Belt as an untouched haven is mostly unfounded. With the remaining wildlife clinging on in hedgerows and in pockets of lush woodland, Areas of Outstanding Natural Beauty (AONBs), and Sites of Specific Scientific Interest (SSSIs), the Green Belt is for the most part a man-made landscape; a polluted pasture land devoid of biodiversity.

Much of the existing agricultural land is actually in poor condition, with its depleted soils heavily reliant on chemical fertiliser, fungicides and pesticides, which have a damaging effect on biodiversity, key insect pollinators, rivers and ground water sources.

Large swathes of Green Belt land are also unproductive, and are disguised as agricultural in order to collect farm subsidies. Land banking is also common, in the hope that landowners can cash in on speculative development on their land. The Green Belt arguably preserves privilege, whilst many get crippled by appallingly high rent in the urban area.

As well as being unaffordable, London is rabidly hungry. Huge quantities of food produce are imported into London from the world over, clocking up food miles and emissions, which is inherently unsustainable and wasteful. In this respect, the Green Belt surrounding London is an untapped resource; an available space available ideal for the formation of a peri-urban agroecological system.

Broadly speaking, agroecology is the science of applying ecological concepts and principles to the design, development, and management of sustainable agricultural systems. Along with the release of appropriate Green Belt land for housing and associated infrastructure (schools, roads, essential amenities etc) in close proximity to London and existing transport nodes, a wider vision of the function of the Green Belt is required.

Utilising the Green Belt to produce a portion of the food consumed within London, for example, is an astute spatial adaptation. Indeed, not only would this reduce emissions by sourcing produce locally but with the use of agroecology, it is also an opportunity to provide nutritious organic produce, improve soil health, increase biodiversity, and create jobs.

Agroecology is a form of organic farming whose main ethos is the production of a diversified yield of crops without the use of pesticides or chemical fertiliser. The Food & Agriculture Organisation (FAO) of the UN held an International symposium on agroecology in April 2018 in which it stressed the need to scale up agroecology initiatives so as to meet UN sustainable development goals (SDGs).

In many ways it is an insurgent form of agriculture that goes against the tide of intensive industrial scale farming, minimises human impact and works in symbiosis with local ecosystems – enhancing the synergy between plants, insects, crops and soil fertility. Adopting these methods would bolster climate change resilience and considerably alleviate food insecurity.

Releasing strategic areas of land for housing in transport corridors and nodes as well as within and bordering existing Green Belt settlements should be accompanied by the implementation of a closed-loop agroecological system on suitable land. Such a system could also go hand-in-hand with sustainable waste management: the tons of biodegradable food waste generated in the city can be utilised to provide organic fertiliser or biogas through anaerobic digestion.

Incentives should be given to landowners and farmers to reforest parcels of barren land and they should be encouraged to diversify their crops and adopt agroecological principles. The benefits of this would be multi-pronged from an ecological perspective as well as from an economic and social perspective. It would contribute towards more self-sufficiency instead of a reliance on imported foods.

Reform of Green Belt policy is long overdue. The fundamental planning tenets of Green Belt policy – namely limiting sprawl, settlement coalescence, maintaining “openness”, and assisting in urban regeneration through the recycling of brownfield land can still be preserved but should also be questioned.

What is “openness” for example if it means preserving a sterile green desert and presiding over an insect Armageddon? The national planning system and other relevant bodies and policy-makers should give the Green Belt a wider role in the sustainable urban management of Greater London.

Thomas Courtney is a Bedford-based town planner, writing in a personal capacity.


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.