ETH, STP

Cornerstone Course – Day 2: Climate Change II

Climate Change History:

Emission trends are not disputed any more. However, they can be viewed in different lights. Either emissions include production emission (i.e. are counted at the end user), or emission are only counted where and when they happen (i.e. caunted locally when they happen).

Policy-wise the focus increased beyond mitigation to include adaptation. Previously, adaptation was seen as post-colonial compensation. It spins off from impacts research. The focus was on slow changes in rising temperature and climates shift. However, extreme weather frequency increased and therefore it became a more urgent problem. Extreme weather effects take up the focus as they are news-worthy and can be directly felt.

Adaptation is becoming more mainstream as it is considered in existing policy fields and new knowledge is communicated. Adaptation as a field of its own is absorbed by the mainstream.

Current Climate Change:

Mitigation is the main field and the idea is to reduce the impact. It is summarised as a formula of

Impact = Population * consumption * energy intensity * carbon intensity

Consumption and population are difficult to change as religious and capitalist influences dictate most of their development. The focus lies on technology. Technology could improve energy intensity and carbon intensity.

Non-energy solutions range from reducing methane production in cattle to refrigeration for foods (to reduce food waste). Agricultural technologies is dominated by deforestation reduction. Energy solution cover heat usage, power usage and mobility usage. The main idea is to switch mobility and heat electricity-driven technology.

Four fields offer themselves up: electric heating and mobility systems, sustainable energy supply, alternative fuels, efficiency increases.

Mitigation Scenarios:

Stabilisation scenarios are provided by the IPCC without telling how to get there. Each scenario just tell us where we end up with specific CO2 concentrations in the atmosphere. However, it is hard to explain them and most interpretations of IPCC’s graphs are plainly wrong.

Mitigation policy approaches:

Three example policies:

Tragedy of The Commons: We have to limit the amount of CO2 in the atmosphere, but each single “player” wants to emit as much CO2 as possible. However, a solution is yet to be found. Other “commons problems” can be used as comparison. Many solutions can be found (per the multiplier listed above). However, the formulation sets up a zero-sum-game which results in adversarial distribution politics. Another thing is that ideological differences may impede the manipulation of some multipliers. Also free-rider and cheating must be handled.

Energy Transition: The requirement is to go from technology set A to technology set B. It can be framed as a win/win situation. It neatly focuses on carbon intensity and energy intensity. It also offers opportunities for single nations to develop profitable front-runner industry (e.g. Wind and Solar in Germany). However, carbon intensity can probably not get to zero. Another downturn could be that improved carbon intensity could make other impacts worse. E.g. in semi-conductors more than 70 elements are used and to fully recycle them large amounts of energy need to separate reduce the value of separating in the first place.

Policy instruments:

Scale dictates what kind of policy instrument can be used:

  1. Global commons problems require global agreements.
  2. Technological transitions require national strategies.
  3. Co-benefits of change are associated with sectorial policies.
  4. Technological and behavioural changes are underlying of all.

Each instrument offers different advantages and disadvantages:

Policy Instruments

 Disin-
centives
<--- -->In-
centives
TypeCriminal-
ise
behaviour
Tax activ-
ities
Generate inform-
ation
Subsidise activitiesBuild and buy things
Instru-
ment
Techno-
logy standards
Carbon taxesResearch and develop-
ment
Tax creditsRenew-
ables quotas
Instru-
ment
Perform-
ance standards
Emissions trading marketsProduct labellingFeed-in tariffsPublic infra-
structure
Ad-
vantages
Effective and easy to enforceEfficient way to reduce emissionsGovern-
ment creating public goods
Effective way to diffuse techno-
logies
Predict-
able way to diffuse techno-
logies
Disad-
vantages
Inefficient and against spirit of choicePolitically challeng-
ing and/or difficult to imple-
ment
Hard to tell whether it has any effectLittle effect on emission, govern-
ment picks winners*)
May be in-
efficient, smells of socialism *)
*) Some of the advantages and disadvantages are politically loaded. The assumption that markets allocate better than governments (Mazzucato, The Entrepreneurial State, 2013) has been challenged and Socialism is a derogative term to discredit opponents.

Politics is not regarded as the job of scientists, however, studying politics could be. Most scientists try to keep their own opinions and biases out of their products which is much harder than it sounds.

Climate science uncertainties are seen in a new context when they enter the area policy:

  • Pardigmatic: Are we asking questions from the right angle?
  • Translational: Can we explain our findings to people without losing nuances?

Two case studies in Solar Development in Germany where two groups support solar technology, but disagree with one another.

Desertec: Large-scale PV (cheap) in Africa and distribute it to Germany driven by technology companies and as a co-benefit more integration between Europe and MENA. Failure is blamed on political will and support.

Eurosolar: Small-scale lcoal PV (rooftops) to gain energy autonomy and were drivven by grass-roots.  Failure of the project was blamed on resistance by the “incumbent fossil-nuclear complex”.

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ETH, STP

Cornerstone Course – Day 2: Climate Change I

The focus of the afternoon is Argument Analysis applied to environmental decisions, specifically Climate Change.

Part 1 – Modelling:

Understanding modelling by the example of the oblique throw in football:

A target system describes what we want to achieve (e.g. getting the ball to a specific location). Based on the target system a conceptual model can be devised. Specifically, we idealise properties that allow us to model the specific target system (weight, but not colour).

Model equations (e.g. Newton’s laws) are selected, parametrized (e.g. wind) and implemented. The implementation must consider numeric code, parameter values, initial conditions and boundary conditions.

Running the implementation produces a simulation results which is  deterministic *).

The problems focus on structural model uncertainty (uncertainty of numerical code, parameters, initial conditions and boundary conditions) and deductive uncertainties.

*) Up to randomization and parallelisation effects.

Part 2 – Argue with uncertain information:

An important logic puzzle to consider is the Wason Selection Task as it shows difficulties with reasoning, even in relatively simple case.s

We argue if it is controversial whether a statement is true or not. Further we want to show that our reasoning is sound. Most arguments are deductive, but also non-deductive arguments are possible.

The standard form of arguments is inference. Premises are used to justify a conclusion. The correctness of premises as well as the inference is debatable and needs to be confirmed to accept the conclusion.

Argument analysis begins with a complete arguments and follows these steps:

  1. Reconstructing arguments: Identify premises and conclusions in the argument.
  2. Evaluating arguments: Are premises true or not and can the relationship between premises and conclusion be proven correct? Deductive arguments can be formally verified, whereas in non-deductive arguments there validity cannot be confirmed by correctness. Premises do not guarantee the conclusion  (i.e. they are probabilistic). Fallacious arguments can follow if premises are too weak to support the conclusion. Weakness can be caused by critical points:
    • Inductive inferences
      • incomplete information
      • sample sizes, representativeness
      • Wrong intuitions about probability
    • Causal arguments
      • Inappropriate concept of causality (single/multiple causes; feedback)
      • Incomplete information
      • inference from mere positive correlation, or temporal sequence
    • Arguments by analogy
      • Incomplete Information
      • Illustrative or relevant (dis-)analogy of different strength

A (logical) side note:

On sufficiency and necessity:

In the assertion of the form “If A, then B”:

  • A (true) is a sufficient condition for B (true) .
  • B (true) is a necessary condition for A (true): “If not B, then not A”.

The assertion “If and only if A, then B”:

  • A is sufficient and necessary condition for B, and vice versa.

In an assertion of the form “Only if A, then B”:

  • A(true) is a necessary condition for B (true)
  • B (true) is a sufficient condition for A (true). “If not B, then not A”.

On conditionals:

(to be filled in)

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