In China’s three big population centres—the areas around Beijing in the north, Shanghai in the east and Guangzhou, the capital of Guangdong province, in the south—life and work have started to follow the sinews of the high-speed rail system. Trains were previously too infrequent, too slow and too crowded to allow for daily commutes. Now, each of these three mega-cities is developing commuter corridors. Little wonder: house prices in satellite towns and cities tend to be much cheaper. In Kunshan, for example, homes cost about 70% less than in nearby Shanghai. But the bullet train between the two cities takes just 19 minutes and costs a mere 25 yuan ($3.60). And Kunshan is just one of many options for those seeking to escape Shanghai’s high costs. There are now about 75 million people living within an hour of Shanghai by high-speed rail.
Surveys show that more than half of passengers on the busiest lines are “generated traffic”—that is, people making trips that they would not have made before. This is unquestionably good for the economy. It means the trains are expanding the pool of labour and consumers around China’s most productive cities, while pushing investment and technology to poorer ones. Xu Xiangshang, a dapper businessman, oversees sales of apartments built next to high-speed railway stations in less well-off parts of Anhui. These are less than half an hour from Nanjing, a prosperous city of 8m that is the capital of Jiangsu province. “Bullet trains are becoming just like buses,” he says.
The overall bill is already high. China Railway Corporation, the state-owned operator of the train system, has debts of more than 4 trillion yuan, equal to about 6% of GDP.
Less than a decade ago China had yet to connect any of its cities by bullet train. Today, it has 20,000km (12,500 miles) of high-speed rail lines, more than the rest of the world combined. It is planning to lay another 15,000km by 2025. Just as astonishing is urban growth alongside the tracks. At regular intervals—almost wherever there are stations, even if seemingly in the middle of nowhere—thickets of newly built offices and residential blocks rise from the ground.
But the network expansion now under way is even bolder than Mr Liu had envisaged. China has a four-by-four grid at present: four big north-south and east-west lines. Its new plan is to construct an eight-by-eight grid by 2035. The ultimate goal is to have 45,000km of high-speed track. Zhao Jian of Beijing Jiaotong University, who has long criticised the high-speed push, reckons that only 5,000km of this will be in areas with enough people to justify the cost. “With each new line, the losses will get bigger,” he says.
Some analysts question how much of it has been wisely spent. In a widely circulated study published last autumn, Atif Ansar of Oxford University’s Saïd Business School and his co-authors say the world’s “awe and envy” is misplaced. More than half of China’s infrastructure projects have “destroyed economic value”, they reckon. Their verdict is based on 65 road and rail projects backed by the Asian Development Bank (ADB) or the World Bank since the mid-1980s. Thanks to the banks’ involvement, these projects are well documented.
The ADB expected the Yuanjiang-Mohei highway (Yuan-Mo for short) to cut travel times, reduce traffic accidents and lower the costs of fuelling and repairing vehicles, adding up to a compelling economic return of 17.4% a year. By 2004, however, traffic was 49% below projections and costs were more than 20% over budget, thanks to unforgiving terrain prone to landslides.
As a rule, the ADB and World Bank will approve an undertaking only if they expect its broad benefits (the economic gains from reduced travel times, fewer accidents, etc) to exceed its costs by a large margin, leaving ample room for error. Mr Ansar and his co-authors assume this margin is 40%: they posit a ratio of expected benefits to costs of 1.4 for every project. They scoured the banks’ review documents for examples of cost overruns and traffic shortfalls. Given these assumptions, a project becomes unviable if costs overrun by more than 40%, traffic undershoots by 29%, or some combination of the two. Of the 65 projects, 55% fell into this category.
The authors’ conclusion, however, rests on their assumption about the margin for error built into the projects they looked at. Take Yuan-Mo, for example. Its projected benefits, over its first 20 years of operation, were several times greater than its costs. But as often with roads, the costs arrive early; the benefits are spread thinly over many years
At a discount rate equal to China’s borrowing costs only 8% of the project are not economic and HSR traffic gains can lag for a few years
It is necessary to reduce the future payoffs by some annual percentage, known as a “discount rate”. The higher this is, the lower the value placed today on tomorrow’s gains.
So a lot turns on what rate is chosen. For historical reasons, the ADB adopts a high one of 12%. At that rate, Yuan-Mo’s ratio of expected benefits to costs equals 1.5, roughly in line with the authors’ assumptions.
But at a gentler rate of 9%, the ratio improves to about 2.
At a rate of 5.3% (more in line with government borrowing costs) the ratio rises to 3.
With these higher margins for error, many fewer elephants turn white. At a ratio of 2, the share falls to 28%. If the ratio is assumed to be 3, the proportion of duds falls to just 8%.
The authors also assume that any traffic shortfall persists throughout its life. That is not always the case. Traffic on Yuan-Mo, for example, has rebounded, according to the road’s operator. By 2015 it was 31% higher than the ADB projected back in 1999. Around last year’s lunar new-year holiday the road handled record numbers. Some white elephants turn grey with age.
In the long view of 10-20 years there is an economic payoff vs China’s borrowing costs and long term improvements to the country
SOURCE – Economist
Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.
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