Session 2 Flashcards
Why is it hard to determine the optimal level of pollution and find ways to achieve it?
Because pollution is an externality, if it weren’t, these questions would automatically be taken care of by the market.
What is economic efficiency?
An allocation of goods is economically efficient if any change that would benefit somebody would harm somebody else.
-> social welfare cannot be increased by a voluntary trade between agents
What is economic optimality and how does it compare to economic efficiency?
An allocation is economically optimal, if it maximizes social welfare.
Is a stronger criteria than economic efficiency, therefore harder to implement since more information is required & redistribution of wealth requires idealogical standpoint.
What criteria do real world policy instruments use?
Mix of criteria:
- economic efficiency (hard to formulate because lack of information)
- sustainability
- health or safety considerations
- equity
- political feasibility
What are flow pollutants?
Name examples
- cause damage by flow of pollution
- if source of pollution is removed, damage stops
- examples: noise, light
What are stock pollutants?
What challenges do they pose?
Name examples
- cause damage by cumulative stock of pollutant
- cause of most pollution problems
- stock degrades with time
- challenge: modelling stocks and flows
- examples: climate active gases, heavy metals
What characterizes uniformly mixing pollutants?
Name examples
- quick despersion to uniform spatial distribution
- location does not matter for damage assesment
- examples: CO² emissions, chloroflourocarbon emissions (FCKW)
What characterizes non-uniformly mixing pollutants?
What challenges do they pose?
Name examples
- do not spread out uniformly
- pollution concentrations and damages vary from place to place
- challenges: areal differences
- examples: ozone accumulation in lower atmosphere, pollutants from diesel emissions (NOx), pollution of local water bodies
What is CO²?
flow pollutant, stock pollutant
uniformly mixing pollutant, non-uniformly mixing pollutant
uniformly mixing
stock (if emissions exceeds absorbtion of plants and oceans, stock builds up)
What is the optimal level of pollution?
Why pollute at all?
- producing certain goods may require some pollution
- completely pollution free production might be very expensive
- zero pollution is usually neither economically optimal, nor efficient
- trade-off between benefits and costs of pollution has to be made
Uniformly mixing flow pollutants: How do we find the optimal level of pollution?
- setting up the benefit curves of pollution
- see benefits of pollution as avoiding abatement costs if pollution limit is imposed on company
- setting up damage curves of pollution
- determining the optimal level of pollution by maximizing net benefits (equal to equalizing marginal benefits and damages)
- calculate cost, benefit and welfare of optimal pollution level
How can it be argued that emissions abatement cause side (double) benefits?
weak double benefit hypothesis: proceeds of policy instruments for emission reductions are used to reduce distorionary taxes -> economy as a whole profits
strong double benefit hypothesis: regulatory interventions nudges firms to implrove their operations (new technologies)
-> more nuanced view of abatement function necessary
What happens when trade-off between damages and costs seems inappropriate?
- happens often when human health is at stake
- marginal damage can be modelled as kink function (umgedrehtes L)
- in practice, trade-offs are always considered (implicitly putting a value on a human life)
How do uniformly mixing stock pollutants differ from uniformly mixing flow pollutants in regards to the calculation of optimal amount of pollution?
- benefit depends on flow while damage depends on stock -> need to model relationship between the two over time
- policy makers can only control the flow of pollutants
- stock accumulates over time -> time needs to be modelled
- > larger decay rate & discount rates imply larger pollution
What are problems in reality with modelling benefit & damage functions as convex/concave functions?
- convex optimization problems have unique solution, but reality more complex
- e.g. threshold from certain value onwards, combination of multiple curves
- if functions not convex, information requirements are much higher. this information is hard to get in environmental regulation