half of the world’s greenhouse gas emissions. In a study published in Nature Scientific Reports, we reveal just how deep this injustice runs.
Developed nations such as Australia, the United States, Canada, and European countries are essentially climate ―free-riders‖: causing the majority of the problems through high greenhouse gas emissions, while incurring(招致) few of the costs such as climate change’s impact on food and water. In
other words, a few countries are benefiting enormously from the consumption of fossil fuels, while at the same time contributing disproportionately to the global burden of climate change.
On the flip side, there are many ― forced riders‖, who are suffering from the climate change impacts despite having scarcely contributed to the problem. Many of the world’s most climate- vulnerable countries, the majority of which are African or small island states, produce a very small quantity of emissions. This is much like a non-smoker getting cancer from second-hand smoke, while the heavy smoker is fortunate enough to smoke in good health.
The Paris agreement has been widely hailed as a positive step forward in addressing climate change for all, although the details on addressing ―climate justice‖ can be best described as sketchy.
The goal of keeping global temperature rise ―well below‖ 2 degree is commendable(值得称赞的) but
the emissions- reduction pledges submitted by countries leading up to the Paris talks are very unlikely to deliver on this.
More than $100 billion in funding has been put on the table for supporting developing nations to reduce emissions. However, the agreement specifies that there is no formal distinction between developed and developing nations in their responsibility to cut emissions, effectively ignoring historical emissions. There is also very little detail on who will provide the funds or, importantly, who is responsible for their provision. Securing these funds, and establishing who is responsible for raising them will also be vital for the future of climate-vulnerable countries.
The most climate-vulnerable countries in the world have contributed very little to creating the global disease from which they now suffer the most. There must urgently be a meaningful mobilization(组织，动员) of the policies outlined in the agreement if we are to achieve national emissions reductions while helping the most vulnerable countries adapt to climate change.
And it is clearly up to the current generation of leaders from high-emitting nations to decide whether they want to be remembered as climate change tyrants or pioneers.
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1. The author is critical of the Paris climate agreement because. A) it is unfair to those climate-vulnerablenations
B) it aims to keep temperature rise belowonly
C) it is beneficial to only fewer than 4% ofcountries
D) it burdens developed countries with the soleresponsibility
2. Why does the author compare the ―forced riders‖ to second-handsmokers?
A) They have little responsibility for public healthproblems.
B) They are easily affected by unhealthy environmentalconditions.
C) They have to bear consequences they are not responsiblefor.
D) They are unaware of the potential risks they arefacing.
3. What does the author say about the $ 100 billionfunding?
A) It will motivate all nations to reduce carbonemissions.
B) There is no final agreement on where it will comefrom.
C) There is no explanation of how the money will bespent.
D) It will effectively reduce greenhouse emissionsworldwide.
4. What urgent action must be taken to realize the Paris climateagreement?
A) Encouraging developing nations to take theinitiative.
B) Calling on all the nations concerned to make jointefforts.
C) Pushing the current world leaders to reachagreement.
D) Putting in effect the policies in the agreement atonce.
(B)
With the coming of big data age, data science is supposed to be starved for, of which the adaption can point a profound change in corporate competitiveness. Companies, both born in digital era and traditional world are showing off their skills in data science. Therefore, it seems to have been creating a great demand for the experts of this type.
Mr Carlos Guestrin, machine learning professor from university of Washington argues that all software applications will need inbuilt intelligence within five years, making data scientists – people
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trained to analyze large bodies of information – key workers in this emerging ―cognitive‖ technology economy. There are already critical applications that depend on machine learning, a subfield of data science, led by recommendation programs, fraud detection system, forecasting tools and applications for predicting customer behavior.
Many companies that born digital – particularly internet companies that have a great number of real- time customer interactions to handle – are all-in when it comes to data science. Pinterest, for intense, maintains more than 100 machine learning models that could be applied to different classed of problems, and it constantly fields requests from managers eager to use this resource to deal with their business problem.
The most important factor weighing on many traditional companies will be the high cost of launching a serious machine-learning operation. Netflix is estimated to spend $150m a year on a single application and the total bill is probably four times that once all its uses of the technology are taken into account.
Another problem for many non-technology companies is talent. Of the computer science experts who use Kaggle, only about 1,000 have deep learning skills, compared to 100,000 who can apply other machine learning techniques, says MrGoldbloom. He adds that even some big companies of this type are often reluctant to expand their pay scales to hire the top talent in this field.
A third barrier to adapting to the coming era of ―smart‖ applications, however, is likely to be cultural. Some companies, such as General Electric, have been building their own Silicon Valley presence to attract and develop the digital skills they will need.
Despitetheobstacles,somemaymasterthisdifficulttransition.Butcompaniesthatwerebuilt,fromthe beginning, with data science at their center, are likely to represent seriouscompetition. 1. What cannot be inferred from the passage about the machinelearning?
A. Machine learning operations are costly inNetflix.
B. Machine learning plays an important role in existentapplications.
C. Machine learning experts are not highly paid in some non-technologycompanies.
D. Machine learning models are not sufficient to solve business problems inPinterest.
2. The underlined word in the 3rdparagraph ―fields‖ mostprobablymeans
. A. avoids
B.creates C.solves D.classifies.
3. Which one is the biggest obstacle for many traditional companies to begin a machine-learningoperation? A. Highcost
B. Expertcrisis.
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C. Technologicalproblems
D. Customerinteractions.
D C A
(C)
Dr. Donald Sadoway at MIT started his own battery company with the hope of changing the world’s energy future. It’s a dramatic endorsement for a technology most people think about only when their smartphone goes dark. But Sadoway isn’t alone in boasting energy storage as a missing link to a cleaner, more efficient, and more equitable energy future.
Scientists and engineers have long believed in the promise of batteries to change the world. Advanced batteries are moving out of specialized markets and creeping into the mainstream, signaling a tipping point for forward-looking technologies such as electric cars and rooftop solar panels.
The ubiquitous （无所不在的）battery has already come a long way, of course. For better or
worse, batteries make possible our mobile-first lifestyles, our screen culture, our increasingly globalized world. Still, as impressive as all this is, it may be trivial compared with what comes next. Having already enabled a communications revolution, the battery is now poised to transform just about everything else.
The wireless age is expanding to include not just our phones, tablets, and laptops, but also our cars, homes, and even whole communities. In emerging economies, rural communities are bypassing the wires and wooden poles that spread power. Instead, some in Africa and Asia are seeing their first lightbulbs illuminated by the power of sunlight stored in batteries.
Today, energy storage is a $33 billion global industry that generates nearly 100 gigawatt-hours of electricity per year. By the end of the decade, it’s expected to be worth over $50 billion and generate 160 gigawatt-hours, enough to attract the attention of major companies that might not otherwise be interested in a decidedly pedestrian technology. Even utility companies, which have long viewed batteries and alternative forms of energy as a threat, are learning to embrace the technologies as enabling rather than disrupting.
Today’s battery breakthroughs come as the world looks to expand modern energy access to the billion or so people without it, while also cutting back on fuels that warm the planet. Those simultaneous challenges appear less overwhelming with increasingly better answers to a centuries-old question: how to make power portable.
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