Australia is an important player in the global tourism business. In 2016, 8.7 million visitors arrived in Australia and 8.8 million Australians went overseas. A further 33.5 million overnight trips were made domestically.
But all this travel comes at a cost. According to the Global Sustainable Tourism Dashboard, all Australian domestic trips and one-way international journeys (the other half is attributed to the end point of travel) amount to 15 million tonnes of carbon dioxide for 2016. That is 2.7% of global aviation emissions, despite a population of only 0.3% of the global total.
The peak month of air travel in and out of Australia is December. Christmas is the time where people travel to see friends and family, or to go on holiday. More and more people are aware of the carbon implications of their travel and want to know whether, for example, they should purchase carbon offsets or not.
Our recent study in the Journal of Air Transport Management showed that about one third of airlines globally offer some form of carbon offsetting to their customers. However, the research also concluded that the information provided to customers is often insufficient, dated and possibly misleading. Whilst local airlines Qantas, Virgin Australia and Air New Zealand have relatively advanced and well-articulated carbon offset programs, others fail to offer scientifically robust explanations and accredited mechanisms that ensure that the money spent on an offset generates some real climate benefits.
The notion of carbon compensation is actually more difficult than people might think. To help explain why carbon offsetting does make an important climate contribution, but at the same time still adds to atmospheric carbon, we created an animated video clip.
The video features Jack, a concerned business traveller who begins purchasing carbon credits. However, he comes to the realisation that the carbon emissions from his flights are still released into the atmosphere, despite the credit.
The concept of “carbon neutral” promoted by airline offsets means that an equal amount of emissions is avoided elsewhere, but it does not mean there is no carbon being emitted at all – just relatively less compared with the scenario of not offsetting (where someone else continues to emit, in addition to the flight).
This means that, contrary to many promotional and educational materials (see
here for instance), carbon offsetting will not reduce overall carbon emissions. Trading emissions means that we are merely maintaining status quo.
A steep reduction, however, is what’s required by every sector if we were to reach the net-zero emissions goal by 2050, agreed on in the Paris Agreement.
Carbon offsetting is already an important “polluter pays” mechanism for travellers who wish to contribute to climate mitigation. But it is also about to be institutionalised at large scale through the new UN-run Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA).
CORSIA will come into force in 2021, when participating airlines will have to purchase carbon credits for emissions above 2020 levels on certain routes.
The availability of carbon credits and their integrity is of major concern, as well as how they align with national obligations and mechanisms agreed in the Paris Agreement. Of particular interest is Article 6, which allows countries to cooperate in meeting their climate commitments, including by “trading” emissions reductions to count towards a national target.
The recent COP23 in Bonn highlighted that CORSIA is widely seen as a potential source of billions of dollars for offset schemes, supporting important climate action. Air travel may provide an important intermediate source of funds, but
ultimately the aviation sector, just like anyone else, will have to reduce their own emissions. This will mean major advances in technology – and most likely a contraction in the fast expanding global aviation market.
In the meantime, and if you have booked your flights for Christmas travel, you can do the following:
pack light (every kilogram will cost additional fuel)
minimise carbon emissions whilst on holiday (for instance by biking or walking once you’re there), and
support a credible offsetting program.
And it’s worth thinking about what else you can do during the year to minimise emissions – this is your own “carbon budget”.
Susanne Becken, Professor of Sustainable Tourism and Director, Griffith Institute for Tourism, Griffith University and Brendan Mackey, Director of the Griffith Climate Change Response Program, Griffith University
Two new exoplanets have been discovered thanks to NASA’s collaboration with Google’s artificial intelligence (AI). One of those in today’s announcement is an eighth planet – Kepler-90i – found orbiting the Sun-like star Kepler-90. This makes it the first system discovered with an equal number of planets to our own Solar system.
A mere road trip away, at 2,545 light-years from Earth, Kepler-90i orbits its host star every 14.4 Earth days, with a sizzling surface temperature similar to Venus of 426°C.
The new exoplanets are added to the growing list of known worlds found orbiting other stars.
This new Solar system rival provides evidence that a similar process occurred within Kepler-90 that formed our very own planetary neighbourhood: small terrestrial worlds close to the host star, and larger gassy planets further away. But to say the system is a twin of our own Solar system is a stretch.
The entire Kepler-90 system of eight planets would easily fit within Earth’s orbit of the Sun. All eight planets, bar Kepler-90h, would be too hostile for life, lying outside the so-called habitable zone.
Evidence also suggests that planets within the Kepler-90 system started out farther apart, much like our own Solar system. Some form of migration occurred, dragging this system inwards, producing the orbits we see in Kepler-90 today.
Google’s collaboration with NASA’s space telescope Kepler mission has now opened up new and exciting opportunities into AI helping with scientific discoveries.
So how exactly did Google’s AI discover these planets? And what sort of future discoveries can this technology provide?
Traditionally, software developers program computers to perform a particular task, from playing your favourite cat video, to determining exoplanetary signals from space based telescopes such as NASA’s Kepler Mission.
These programs are executed to serve a single purpose. Using code intended for cat videos to hunt exoplanets in light curves would lead to some very interesting, yet false, results.
Googles’s AI is programmed rather differently, using machine learning. In machine learning, AI is trained through artificial neural networks – somewhat replicating our brain’s biological neural networks – to perform tasks like reading this article. It then learns from its mistakes, becoming more efficient at its particular task.
Google’s DeepMind AI, AlphaGo, was trained previously to play Go, an extremely complex yet elegant Chinese board game. Last year, AlphaGo defeated Lee Sedol, the world’s best Go player, by four games to one. It simply trained itself by watching thousands of previously played games, then competing against itself.
In our exoplantary case, AI was trained to identify transiting exoplanets, sifting through 15,000 signals from the Kepler exoplanet catalogue. It learned what was and wasn’t a signal caused by an exoplanet eclipsing its host star. These 15,000 signals were previously vetted by NASA scientists prior to the AI’s training, guiding it to a 96% efficiency of detecting known exoplanets.
Researchers then directed their AI network to search in multiplanetary systems for weaker signals. This research culminated in today’s announcement of both Kepler-90i and another Earth-sized exoplanet, Kepler-80g, in a separate planetary system.
Google’s AI has analysed only 10% of the 150,000 stars NASA’s Kepler Mission has been eyeing off across the Milky Way galaxy.
There’s potential then for sifting through Kepler’s entire catalogue and finding other exoplanetary worlds that have either been skimmed by scientist or haven’t been checked yet, due to Kepler’s rich data set. And that’s exactly what Google’s researchers are planning to do.
Machine learning neural networks have been assisting astronomers for a few years now. But the potential for AI to assist in exoplanetary discoveries will only increase within the next decade.
The Kepler mission has been running since 2009, with observations slowly coming to an end. Within the next 12 months, all of its on-board fuel will be fully depleted, ending what has been, one of the greatest scientific endeavours in modern times.
Kepler’s successor, the Transiting Exoplanet Survey Satellite (TESS) will be launching this coming March.
TESS is predicted to find 20,000 exoplanet candidates during its two-year mission. To put that into perspective, in the past 25 years, we’ve managed to discover just over 3,500.
This unprecedented inundation of exoplanetary data needs to either be confirmed by other transiting observations or other methods such as ground-based radial velocity measurements.
There just isn’t enough people-power to sift through all of this data. That’s why these machine learning networks are needed, so they can aid astronomers in sifting through big data sets, ultimately assisting in more exoplanetary discoveries. Which begs the question, who exactly gets credit for such a discovery?
At a time when the effects of climate change are accelerating and published science overwhelmingly supports the view that humans are responsible for the rate of change, powerful groups remain in denial across politics, the media, and industry. Now more than ever, we need scientists and policymakers to work together to create and implement effective policy which is informed by the most recent and reliable evidence.
We know that trust between scientists and policymakers is important in developing policy that is informed by scientific evidence. But how do you build this trust, and how do you make sure that it genuinely leads to positive outcomes for society?
In response to these questions, our recent Perspective in Nature Climate Change explores the dynamics of trust at the interface of climate science and policy.
We suggest that while trust is an important component of the science-policy dynamic, there can be such a thing as “too much” trust between scientists and policymakers.
Understanding this dynamic is crucial if we are to deliver positive outcomes for science, policy, and the society that depends on their cooperation.
Trust between climate scientists (researchers in a range of disciplines, institutions, and organisational settings) and policymakers (civil servants in government departments or agencies who shape climate policy) is useful because it enhances the flow of information between them. In a trusting relationship, we can expect to see a scientist explaining a new finding directly to a policymaker, or a policymaker describing future information needs to a scientist.
Together, this arrangement ideally gives us science-led policy, and policy-relevant science.
But as scholars of trust have warned, there is a point beyond which these positive benefits of trust can turn sour.
Think about a hypothetical situation in which a scientist and policy-maker come to trust each other deeply. What happens if one of them starts to become loose with the facts, or fails to adhere to professional standards? Is their trusting counterpart more, or less, likely to identify the poor behaviour and respond appropriately?
Over time, a trusting relationship may evolve into a self-perpetuating belief of trustworthiness based on the history of the relationship. This is where scientists and policymakers may find themselves in a situation of “too much” trust.
We know that science advances by consensus, and that this consensus is shaped by rigorous research and review, and intense debate and scrutiny. But what if (as in the hypothetical example described above) a policy-maker’s trust in an individual scientist means they bypass the consensus and instead depend on that one scientist for new information? What happens if that scientist is – intentionally or unintentionally – wrong?
When you have “too much” trust, the benefits of trust can instead manifest as perverse outcomes, such as “blind faith” commitments between parties. In a situation like this, a policymaker may trust an individual scientist so much that they do not look for signs of misconduct, such as the misrepresentation of findings.
Favouritism and “capture” may mean that some policymakers provide information about future research support only to selected scientists, denying these opportunities to others. At the same time, scientists may promote only their own stream of research instead of outlining the range of perspectives in the field to the policymakers, narrowing the scope of what science enters the policy area.
“Cognitive lock-in” might result, where a policymaker sticks to a failing policy because they feel committed to the scientist who first recommended the course of action. For example, state-of-the-art climate forecasting tools are available in the Pacific but are reportedly underused. This is partly because the legacy of trusting relationships between scientists and policymakers in the region has led them to continue relying on less sophisticated tools.
“Too much” trust can also lead to overly burdensome obligations between scientists and policymakers. A scientist may come to hold unrealistically high expectations of the level of information a policymaker can share, or a policymaker may desire the production of research by an unfeasible deadline.
With this awareness of the potentially negative outcomes of “too much” trust, should we abandon trust at the climate science-policy interface all together?
No. But we can – and should – develop, monitor, and manage trust with acknowledgement of how “too much” trust may lead to perverse outcomes for both scientists and policy-makers.
We should aim for a state of “optimal trust”, which enjoys the benefits of a trusting relationship while avoiding the pitfalls of taking too trusting an approach.
We propose five key strategies for managing trust at the climate science-policy interface.
Be explicit about expectations for trust in a climate science-policy relationship. Climate scientists and policy-makers should clarify protocols and expectations about behaviour through open discussion as early as possible within the relationship.
Transparency and accountability, especially when things go wrong, are critical to achieving and maintaining a state of optimal trust. When things do go wrong, trust repair can right the relationship.
Implement systems for monitoring trust, such as discussion groups within scientific and policy organisations and processes of peer review. Such approaches can help to identify the effects of “too much” trust – such as capture, cognitive lock-in, or unrealistically high expectations.
Manage staff churn in policy and scientific organisations. When scientists or policy-makers change role or institution, handing over the trusting relationships can help positive legacies and practices to carry on.
Use intermediaries such as knowledge brokers to facilitate the flow of information between science and policy. Such specialists can promote fairness and honesty at the science-policy interface, increasing the probability of maintaining ‘optimal trust’.
Embracing strategies such as these would be a positive step toward managing trust between scientists and policymakers, both in climate policy and beyond.
In this time of contested science and highly politicised policy agendas, all of us in science and policy have a responsibility to ensure we act ethically and appropriately to achieve positive outcomes for society.
Rebecca Colvin, Knowledge Exchange Specialist, Australian National University; Christopher Cvitanovic, Research Fellow, University of Tasmania; Justine Lacey, Senior Social Scientist, CSIRO, and Mark Howden, Director, Climate Change Institute, Australian National University