How tackling climate change could tackle inequality

Floods in Fort-de-France, Martinique, after it was hit by Hurricane Maria. Image: Getty.

Inequality is one of the great challenges of this age, and one that will only be exacerbated by climate change. Most pronounced is the problem in cities, where skyscrapers may tower over slums and street vendors hustle outside air-conditioned supermarkets.

But new research has revealed that taking action to reduce greenhouse gas emissions within cities could be one of the great levellers, with the largest social and economic benefits enjoyed by the poor

Energy use alone is responsible for nearly three quarters of global greenhouse emissions, and most energy is consumed by the rich: to drive their cars, heat their buildings and manufacture goods such as refrigerators, air conditioners and televisions – all of which then demand more energy throughout their lifetime. Globally, the wealthiest ten per cent of people may be responsible for more than 50 per cent of emissions.

Though high-income households bear more responsibility for climate change, its most severe impacts will be felt by the poor, who are more likely to live in areas exposed to environmental hazards, such as floodplains or steep slopes, and whose homes may also lack basic infrastructure that might reduce the impacts of extreme weather, such as drains to safely carry away storm-water. Then, in the aftermath of natural disasters, it is the poorest in society who struggle to access the financial resources they need to rebuild their homes and lives, turning a storm into a catastrophe.

But vulnerability to climate change is not just a function of low incomes. Women, for instance, are less likely than men to know how to swim or to be reachable through conventional emergency warning systems, which puts them at greater risk in the event of a flood or storm.

Climate change can therefore compound existing inequalities, further widening the chasm between rich and poor, powerful and powerless.

Having reviewed over 700 studies on transport, buildings and waste management, the research team fom the Coalition for Urban Transitions found that choosing low-carbon options would not only improve public health, create jobs, enhance productivity and cut energy bills, but that many of the gains would be mostly enjoyed by low-income urban residents. Those most vulnerable to climate change are therefore also those who would benefit most from climate action.


Consider outdoor air pollution, which causes around 4.2 million deaths every year, and asthma, bronchitis and other chronic diseases for millions more. This burden of ill-health is overwhelmingly borne by low-income urban dwellers, who more frequently live in polluted areas along highways or near power plants, and are more likely to work outdoors as street vendors, labourers or waste collectors.

Producing electricity from renewables instead of coal, making vehicles more energy efficient, and shifting to lower-carbon fuels for heating and cooking can cut both pollutants and carbon emissions. Since the poor suffer the most from toxic air, they also enjoy the greatest health improvements.

Or consider road safety. More than 1.25 million people die every year from traffic accidents. 90 per cent of whom live in developing countries and nearly half are pedestrians, cyclists or motorcyclists. Many of these people cannot afford a car or even public buses, but face a terrible risk on every trip. Women may face additional constraints, as cultural norms and additional physical risks often deter them from cycling or walking freely around the city.

Segregated walkways and bike lanes are essential to keeping pedestrians and cyclists safe, and the provision of street lighting and street furniture such as benches can further enhance people’s safety by turning the pavements into a place where people want to be. It’s those who are unable to afford any other means of travel that benefit the most, and at the same time, these measures can reduce greenhouse gases by establishing non-motorised transport as a safe, enjoyable way to commute.

The costs of air pollution and road accidents are immense, and overwhelmingly borne by the poor. People are dying because they cannot breathe easily or move safely within cities. This new paper shows that there are opportunities to tackle these everyday inequalities, and simultaneously reduce the risk of dangerous global warming.

Ambitious climate action can therefore lay the foundations for healthier, safer and more equal cities for decades to come.

Sarah Colenbrander is Head of Global Programmes at the Coalition for Urban Transitions and Senior Researcher at the International Institute for Environment and Development. Andrew Sudmant is a Research Fellow at the University of Leeds, and one of the authors of The Economic and Social Benefits of Low-carbon Cities – A Systematic Review of the Evidence.

 
 
 
 

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.