## World Energy Outlook 2015

15 June, 2015

It’s an exciting and nerve-racking time as global carbon emissions from energy production have begun to drop, at least for a little while:

The big international climate negotiations to be concluded in Paris in December 2015 bring these issues to the forefront in a dramatic way. Countries are already saying what they plan to do: you can read their Intended Nationally Determined Contributions online!

But it’s hard to get an overall picture of the situation. Here’s a new report that helps:

• International Energy Agency, World Energy Outlook Special Report 2015: Energy and Climate Change.

Since the International Energy Agency seems intelligent to me, I’ll just quote their executive summary. If you’re too busy for even the executive summary, let me summarize the summary:

Given the actions that countries are now planning, we could have an increase of around 2.6 °C over preindustrial temperature by 2100, and more after that.

### Executive summary

A major milestone in efforts to combat climate change is fast approaching. The importance of the 21st Conference of the Parties (COP21) – to be held in Paris in December 2015 – rests not only in its specific achievements by way of new contributions, but also in the direction it sets. There are already some encouraging signs with a historic joint announcement by the United States and China on climate change, and climate pledges for COP21 being submitted by a diverse range of countries and in development in many others. The overall test of success for COP21 will be the conviction it conveys that governments are determined to act to the full extent necessary to achieve the goal they have already set to keep the rise in global average temperatures below 2 degrees Celsius (°C), relative to pre-industrial levels.

Energy will be at the core of the discussion. Energy production and use account for two-thirds of the world’s greenhouse-gas (GHG) emissions, meaning that the pledges made at COP21 must bring deep cuts in these emissions, while yet sustaining the growth of the world economy, boosting energy security around the world and bringing modern energy to the billions who lack it today. The agreement reached at COP21 must be comprehensive geographically, which means it must be equitable, reflecting both national responsibilities and prevailing circumstances. The importance of the energy component is why this World Energy Outlook Special Report presents detailed energy and climate analysis for the sector and recommends four key pillars on which COP21 can build success.

#### Energy and emissions: moving apart?

The use of low-carbon energy sources is expanding rapidly, and there are signs that growth in the global economy and energy-related emissions may be starting to decouple. The global economy grew by around 3% in 2014 but energy-related carbon dioxide (CO2) emissions stayed flat, the first time in at least 40 years that such an outcome has occurred outside economic crisis.

Renewables accounted for nearly half of all new power generation capacity in 2014, led by growth in China, the United States, Japan and Germany, with investment remaining strong (at $270 billion) and costs continuing to fall. The energy intensity of the global economy dropped by 2.3% in 2014, more than double the average rate of fall over the last decade, a result stemming from improved energy efficiency and structural changes in some economies, such as China. Around 11% of global energy-related CO2 emissions arise in areas that operate a carbon market (where the average price is$7 per tonne of CO2), while 13% of energy-related CO2 emissions arise in markets with fossil-fuel consumption subsidies (an incentive equivalent to $115 per tonne of CO2, on average). There are some encouraging signs on both fronts, with reform in sight for the European Union’s Emissions Trading Scheme and countries including India, Indonesia, Malaysia and Thailand taking the opportunity of lower oil prices to diminish fossil-fuel subsidies, cutting the incentive for wasteful consumption. #### The energy contribution to COP21 Nationally determined pledges are the foundation of COP21. Intended Nationally Determined Contributions (INDCs) submitted by countries in advance of COP21 may vary in scope but will contain, implicitly or explicitly, commitments relating to the energy sector. As of 14 May 2015, countries accounting for 34% of energy-related emissions had submitted their new pledges. A first assessment of the impact of these INDCs and related policy statements (such as by China) on future energy trends is presented in this report in an “INDC Scenario”. This shows, for example, that the United States’ pledge to cut net greenhouse-gas emissions by 26% to 28% by 2025 (relative to 2005 levels) would deliver a major reduction in emissions while the economy grows by more than one-third over current levels. The European Union’s pledge to cut GHG emissions by at least 40% by 2030 (relative to 1990 levels) would see energy-related CO2 emissions decline at nearly twice the rate achieved since 2000, making it one of the world’s least carbon-intensive energy economies. Russia’s energy-related emissions decline slightly from 2013 to 2030 and it meets its 2030 target comfortably, while implementation of Mexico’s pledge would see its energy-related emissions increase slightly while its economy grows much more rapidly. China has yet to submit its INDC, but has stated an intention to achieve a peak in its CO2 emissions around 2030 (if not earlier), an important change in direction, given the pace at which they have grown on average since 2000. Growth in global energy-related GHG emissions slows but there is no peak by 2030 in the INDC Scenario. The link between global economic output and energy-related GHG emissions weakens significantly, but is not broken: the economy grows by 88% from 2013 to 2030 and energy-related CO2 emissions by 8% (reaching 34.8 gigatonnes). Renewables become the leading source of electricity by 2030, as average annual investment in nonhydro renewables is 80% higher than levels seen since 2000, but inefficient coal-fired power generation capacity declines only slightly. With INDCs submitted so far, and the planned energy policies in countries that have yet to submit, the world’s estimated remaining carbon budget consistent with a 50% chance of keeping the rise in temperature below 2 °C is consumed by around 2040—eight months later than is projected in the absence of INDCs. This underlines the need for all countries to submit ambitious INDCs for COP21 and for these INDCs to be recognised as a basis upon which to build stronger future action, including from opportunities for collaborative/co-ordinated action or those enabled by a transfer of resources (such as technology and finance). If stronger action is not forthcoming after 2030, the path in the INDC Scenario would be consistent with an an average temperature increase of around 2.6 °C by 2100 and 3.5 °C after 2200. #### What does the energy sector need from COP21?National pledges submitted for COP21 need to form the basis for a “virtuous circle” of rising ambition. From COP21, the energy sector needs to see a projection from political leaders at the highest level of clarity of purpose and certainty of action, creating a clear expectation of global and national low-carbon development. Four pillars can support that achievement:1. Peak in emissions – set the conditions which will achieve an early peak in global energy-related emissions.2. Five-year revision – review contributions regularly, to test the scope to lift the level of ambition.3. Lock in the vision – translate the established climate goal into a collective long-term emissions goal, with shorter-term commitments that are consistent with the long-term vision.4. Track the transition – establish an effective process for tracking achievements in the energy sector. ### Peak in emissions The IEA proposes a bridging strategy that could deliver a peak in global energy-related emissions by 2020. A commitment to target such a near-term peak would send a clear message of political determination to stay below the 2 °C climate limit. The peak can be achieved relying solely on proven technologies and policies, without changing the economic and development prospects of any region, and is presented in a “Bridge Scenario”. The technologies and policies reflected in the Bridge Scenario are essential to secure the long-term decarbonisation of the energy sector and their near-term adoption can help keep the door to the 2 °C goal open. For countries that have submitted their INDCs, the proposed strategy identifies possible areas for over-achievement. For those that have yet to make a submission, it sets out a pragmatic baseline for ambition. The Bridge Scenario depends upon five measures: • Increasing energy efficiency in the industry, buildings and transport sectors. • Progressively reducing the use of the least-efficient coal-fired power plants and banning their construction. • Increasing investment in renewable energy technologies in the power sector from$270 billion in 2014 to \$400 billion in 2030.

• Gradual phasing out of fossil-fuel subsidies to end-users by 2030.

• Reducing methane emissions in oil and gas production.

These measures have profound implications for the global energy mix, putting a brake on growth in oil and coal use within the next five years and further boosting renewables. In the Bridge Scenario, coal use peaks before 2020 and then declines while oil demand rises to 2020 and then plateaus. Total energy-related GHG emissions peak around 2020. Both the energy intensity of the global economy and the carbon intensity of power generation improve by 40% by 2030. China decouples its economic expansion from emissions growth by around 2020, much earlier than otherwise expected, mainly through improving the energy efficiency of industrial motors and the buildings sector, including through standards for appliances and lighting. In countries where emissions are already in decline today, the decoupling of economic growth and emissions is significantly accelerated; compared with recent years, the pace of this decoupling is almost 30% faster in the European Union (due to improved energy efficiency) and in the United States (where renewables contribute one-third of the achieved emissions savings in 2030). In other regions, the link between economic growth and emissions growth is weakened significantly, but the relative importance of different measures varies. India utilises energy more efficiently, helping it
to reach its energy sector targets and moderate emissions growth, while the reduction of
methane releases from oil and gas production and reforming fossil-fuel subsidies (while
providing targeted support for the poorest) are key measures in the Middle East and Africa, and a portfolio of options helps reduce emissions in Southeast Asia. While universal access to modern energy is not achieved in the Bridge Scenario, the efforts to reduce energy related emissions do go hand-in-hand with delivering access to electricity to 1.7 billion people and access to clean cookstoves to 1.6 billion people by 2030.

## Information and Entropy in Biological Systems (Part 7)

6 June, 2015

In 1961, Rolf Landauer argued that that the least possible amount of energy required to erase one bit of information stored in memory at temperature $T$ is $kT \ln 2,$ where $k$ is Boltzmann’s constant.

This is called the Landauer limit, and it came after many decades of arguments concerning Maxwell’s demon and the relation between information and entropy.

In fact, these arguments are still not finished. For example, here’s an argument that the Landauer limit is not as solid as widely believed:

• John D. Norton, Waiting for Landauer, Studies in History and Philosophy of Modern Physics 42 (2011), 184–198.

But something like the Landauer limit almost surely holds under some conditions! And if it holds, it puts some limits on what organisms can do. That’s what David Wolpert spoke about at our workshop! You can see his slides here:

You can also watch a video:

## Information and Entropy in Biological Systems (Part 6)

1 June, 2015

The resounding lack of comment to this series of posts confirms my theory that a blog post that says “go somewhere else and read something” will never be popular. Even if it’s “go somewhere else and watch a video”, this is too much like saying

Hi! Want to talk? Okay, go into that other room and watch TV, then come back when you’re done and we’ll talk about it.

But no matter: our workshop on Information and Entropy in Biological Systems was really exciting! I want to make it available to the world as much as possible. I’m running around too much to create lovingly hand-crafted summaries of each talk—and I know you’re punishing me for that, with your silence. But I’ll keep on going, just to get the material out there.

Marc Harper spoke about information in evolutionary game theory, and we have a nice video of that. I’ve been excited about his work for quite a while, because it shows that the analogy between ‘evolution’ and ‘learning’ can be made mathematically precise. I summarized some of his ideas in my information geometry series, and I’ve also gotten him to write two articles for this blog:

• Marc Harper, Relative entropy in evolutionary dynamics, Azimuth, 22 January 2014.

• Marc Harper, Stationary stability in finite populations, Azimuth, 24 March 2015.

Here are the slides and video of his talk:

• Marc Harper, Information transport and evolutionary dynamics.

## Information and Entropy in Biological Systems (Part 5)

30 May, 2015

John Harte of U. C. Berkeley spoke about the maximum entropy method as a method of predicting patterns in ecology. Annette Ostling of the University of Michigan spoke about some competing theories, such as the ‘neutral model’ of biodiversity—a theory that sounds much too simple to be right, yet fits the data surprisingly well!

We managed to get a video of Ostling’s talk, but not Harte’s. Luckily, you can see the slides of both. You can also see a summary of Harte’s book Maximum Entropy and Ecology:

• John Baez, Maximum entropy and ecology, Azimuth, 21 February 2013.

Here are his talk slides and abstract:

Abstract. Constrained maximization of information entropy (MaxEnt) yields least-biased probability distributions. In statistical physics, this powerful inference method yields classical statistical mechanics/thermodynamics under the constraints imposed by conservation laws. I apply MaxEnt to macroecology, the study of the distribution, abundance, and energetics of species in ecosystems. With constraints derived from ratios of ecological state variables, I show that MaxEnt yields realistic abundance distributions, species-area relationships, spatial aggregation patterns, and body-size distributions over a wide range of taxonomic groups, habitats and spatial scales. I conclude with a brief summary of some of the major opportunities at the frontier of MaxEnt-based macroecological theory.

Here is a video of Ostling’s talk, as well as her slides and some papers she recommended:

• Annette Ostling, The neutral theory of biodiversity and other competitors to maximum entropy.

Abstract: I am a bit of the odd man out in that I will not talk that much about information and entropy, but instead about neutral theory and niche theory in ecology. My interest in coming to this workshop is in part out of an interest in what greater insights we can get into neutral models and stochastic population dynamics in general using entropy and information theory.

I will present the niche and neutral theories of the maintenance of diversity of competing species in ecology, and explain the dynamics included in neutral models in ecology. I will also briefly explain how one can derive a species abundance distribution from neutral models. I will present the view that neutral models have the potential to serve as more process-based null models than previously used in ecology for detecting the signature of niches and habitat filtering. However, tests of neutral theory in ecology have not as of yet been as useful as tests of neutral theory in evolutionary biology, because they leave open the possibility that pattern is influenced by “demographic complexity” rather than niches. I will mention briefly some of the work I’ve been doing to try to construct better tests of neutral theory.

Finally I’ll mention some connections that have been made so far between predictions of entropy theory and predictions of neutral theory in ecology and evolution.

These papers present interesting relations between ecology and statistical mechanics. Check out the nice ‘analogy chart’ in the second one!

• M. G. Bowler, Species abundance distributions, statistical mechanics and the priors of MaxEnt, Theoretical Population Biology 92 (2014), 69–77.

Abstract. The methods of Maximum Entropy have been deployed for some years to address the problem of species abundance distributions. In this approach, it is important to identify the correct weighting factors, or priors, to be applied before maximising the entropy function subject to constraints. The forms of such priors depend not only on the exact problem but can also depend on the way it is set up; priors are determined by the underlying dynamics of the complex system under consideration. The problem is one of statistical mechanics and it is the properties of the system that yield the correct MaxEnt priors, appropriate to the way the problem is framed. Here I calculate, in several different ways, the species abundance distribution resulting when individuals in a community are born and die independently. In
the usual formulation the prior distribution for the number of species over the number of individuals is 1/n; the problem can be reformulated in terms of the distribution of individuals over species classes, with a uniform prior. Results are obtained using master equations for the dynamics and separately through the combinatoric methods of elementary statistical mechanics; the MaxEnt priors then emerge a posteriori. The first object is to establish the log series species abundance distribution as the outcome of per capita guild dynamics. The second is to clarify the true nature and origin of priors in the language of MaxEnt. Finally, I consider how it may come about that the distribution is similar to log series in the event that filled niches dominate species abundance. For the general ecologist, there are two messages. First, that species abundance distributions are determined largely by population sorting through fractional processes (resulting in the 1/n factor) and secondly that useful information is likely to be found only in departures from the log series. For the MaxEnt practitioner, the message is that the prior with respect to which the entropy is to be maximised is determined by the nature of the problem and the way in which it is formulated.

• Guy Sella and Aaron E. Hirsh, The application of statistical physics to evolutionary biology, Proc. Nat. Acad. Sci. 102 (2005), 9541–9546.

A number of fundamental mathematical models of the evolutionary process exhibit dynamics that can be difficult to understand analytically. Here we show that a precise mathematical analogy can be drawn between certain evolutionary and thermodynamic systems, allowing application of the powerful machinery of statistical physics to analysis of a family of evolutionary models. Analytical results that follow directly from this approach include the steady-state distribution of fixed genotypes and the load in finite populations. The analogy with statistical physics also reveals that, contrary to a basic tenet of the nearly neutral theory of molecular evolution, the frequencies of adaptive and deleterious substitutions at steady state are equal. Finally, just as the free energy function quantitatively characterizes the balance between energy and entropy, a free fitness function provides an analytical expression for the balance between natural selection and stochastic drift.

## Network Theory in Turin

23 May, 2015

Here are the slides of the talk I’m giving on Monday to kick off the Categorical Foundations of Network Theory workshop in Turin:

This is a long talk, starting with the reasons I care about this subject, and working into the details of one particular project: the categorical foundations of networks as applied to electrical engineering and control theory. There are lots of links in blue; click on them for more details!

## Information and Entropy in Biological Systems (Part 4)

21 May, 2015

I kicked off the workshop on Information and Entropy in Biological Systems with a broad overview of the many ways information theory and entropy get used in biology:

• John Baez, Information and entropy in biological systems.

Abstract. Information and entropy are being used in biology in many different ways: for example, to study biological communication systems, the ‘action-perception loop’, the thermodynamic foundations of biology, the structure of ecosystems, measures of biodiversity, and evolution. Can we unify these? To do this, we must learn to talk to each other. This will be easier if we share some basic concepts which I’ll sketch here.

The talk is full of links, in blue. If you click on these you can get more details. You can also watch a video of my talk:

## Information and Entropy in Biological Systems (Part 3)

20 May, 2015

We had a great workshop on information and entropy in biological systems, and now you can see what it was like. I think I’ll post these talks one a time, or maybe a few at a time, because they’d be overwhelming taken all at once.

So, let’s dive into Chris Lee’s exciting ideas about organisms as ‘information evolving machines’ that may provide ‘disinformation’ to their competitors. Near the end of his talk, he discusses some new results on an ever-popular topic: the Prisoner’s Dilemma. You may know about this classic book:

• Robert Axelrod, The Evolution of Cooperation, Basic Books, New York, 1984. Some passages available free online.

If you don’t, read it now! He showed that the simple ‘tit for tat’ strategy did very well in some experiments where the game was played repeatedly and strategies who did well got to ‘reproduce’ themselves. This result was very exciting, so a lot of people have done research on it. More recently a paper on this subject by William Press and Freeman Dyson received a lot of hype. I think this is a good place to learn about that:

• Mike Shulman, Zero determinant strategies in the iterated Prisoner’s Dilemma, The n-Category Café, 19 July 2012.

Chris Lee’s new work on the Prisoner’s Dilemma is here, cowritten with two other people who attended the workshop:

The art of war: beyond memory-one strategies in population games, PLOS One, 24 March 2015.

Abstract. We show that the history of play in a population game contains exploitable information that can be successfully used by sophisticated strategies to defeat memory-one opponents, including zero determinant strategies. The history allows a player to label opponents by their strategies, enabling a player to determine the population distribution and to act differentially based on the opponent’s strategy in each pairwise interaction. For the Prisoner’s Dilemma, these advantages lead to the natural formation of cooperative coalitions among similarly behaving players and eventually to unilateral defection against opposing player types. We show analytically and empirically that optimal play in population games depends strongly on the population distribution. For example, the optimal strategy for a minority player type against a resident tit-for-tat (TFT) population is ‘always cooperate’ (ALLC), while for a majority player type the optimal strategy versus TFT players is ‘always defect’ (ALLD). Such behaviors are not accessible to memory-one strategies. Drawing inspiration from Sun Tzu’s the Art of War, we implemented a non-memory-one strategy for population games based on techniques from machine learning and statistical inference that can exploit the history of play in this manner. Via simulation we find that this strategy is essentially uninvadable and can successfully invade (significantly more likely than a neutral mutant) essentially all known memory-one strategies for the Prisoner’s Dilemma, including ALLC (always cooperate), ALLD (always defect), tit-for-tat (TFT), win-stay-lose-shift (WSLS), and zero determinant (ZD) strategies, including extortionate and generous strategies.

And now for the talk! Click on the talk title here for Chris Lee’s slides, or go down and watch the video:

Abstract. Information theory is an intuitively attractive way of thinking about biological evolution, because it seems to capture a core aspect of biology—life as a solution to “information problems”—in a fundamental way. However, there are non-trivial questions about how to apply that idea, and whether it has actual predictive value. For example, should we think of biological systems as being actually driven by an information metric? One idea that can draw useful links between information theory, evolution and statistical inference is the definition of an information evolving machine (IEM) as a system whose elements represent distinct predictions, and whose weights represent an information (prediction power) metric, typically as a function of sampling some iterative observation process. I first show how this idea provides useful results for describing a statistical inference process, including its maximum entropy bound for optimal inference, and how its sampling-based metrics (“empirical information”, Ie, for prediction power; and “potential information”, Ip, for latent prediction power) relate to classical definitions such as mutual information and relative entropy. These results suggest classification of IEMs into several distinct types:

1. Ie machine: e.g. a population of competing genotypes evolving under selection and mutation is an IEM that computes an Ie equivalent to fitness, and whose gradient (Ip) acts strictly locally, on mutations that it actually samples. Its transition rates between steady states will decrease exponentially as a function of evolutionary distance.

2. “Ip tunneling” machine: a statistical inference process summing over a population of models to compute both Ie, Ip can directly detect “latent” information in the observations (not captured by its model), which it can follow to “tunnel” rapidly to a new steady state.

3. disinformation machine (multiscale IEM): an ecosystem of species is an IEM whose elements (species) are themselves IEMs that can interact. When an attacker IEM can reduce a target IEM’s prediction power (Ie) by sending it a misleading signal, this “disinformation dynamic” can alter the evolutionary landscape in interesting ways, by opening up paths for rapid co-evolution to distant steady-states. This is especially true when the disinformation attack targets a feature of high fitness value, yielding a combination of strong negative selection for retention of the target feature, plus strong positive selection for escaping the disinformation attack. I will illustrate with examples from statistical inference and evolutionary game theory. These concepts, though basic, may provide useful connections between diverse themes in the workshop.