Air pollution and health - GCAM: Difference between revisions
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Note: The documentation of GCAM is 'under review' and is not yet 'published'!
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== Air Pollutant Emissions == | == Air Pollutant Emissions == | ||
Air pollutant emissions such as sulfur dioxide (SO<sub>s</sub>) and nitrogen oxides (NO<sub>x</sub>) are modeled as | Air pollutant emissions (E) such as sulfur dioxide (SO<sub>s</sub>) and nitrogen oxides (NO<sub>x</sub>) are modeled as <math>E_{t}=A_{t}*EF_{t0}*(1-EmCtrl(pcGDP_{t}))</math>where A is activity level, EF is emissions factor, and EmCtrl is a function that represents decreasing emissions intensity as per-capita income increases:<math>EmCtrl_{t}=1-\frac{1}{1+\frac{(pcGDP_{t}-pcGDP_{t0})}{steepness}}</math>where ''pcGDP'' stands for the per-capita GDP, and ''steepness'' is an exogenous constant, specific to each technology and pollutant species, that governs the degree to which changes in per-capita GDP will be translated to emissions controls. The purpose here is to capture the general global trend of increasing pollutant controls over time, but does not capture regional and technological heterogeneity. See the documentation's [http://jgcri.github.io/gcam-doc/emissions.html#air-pollutant-emissions section on air pollution]. | ||
<math>E_{t}=A_{t}*EF_{t0}*(1-EmCtrl(pcGDP_{t}))</math> | |||
where EmCtrl is a function that represents decreasing emissions intensity as per-capita income increases: | |||
<math>EmCtrl_{t}=1-\frac{1}{1+\frac{(pcGDP_{t}-pcGDP_{t0})}{steepness}}</math> | |||
where ''pcGDP'' stands for the per-capita GDP, and ''steepness'' is an exogenous constant, specific to each technology and pollutant species, that governs the degree to which changes in per-capita GDP will be translated to emissions controls. The purpose here is to capture the general global trend of increasing pollutant controls over time, but does not capture regional and technological heterogeneity [ | |||
Revision as of 22:00, 1 September 2020
Note: The documentation of GCAM is 'under review' and is not yet 'published'!| Corresponding documentation | |
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| Previous versions | |
| No previous version available | |
| Model information | |
| Model link | |
| Institution | Pacific Northwest National Laboratory, Joint Global Change Research Institute (PNNL, JGCRI), USA, https://www.pnnl.gov/projects/jgcri. |
| Solution concept | General equilibrium (closed economy)GCAM solves all energy, water, and land markets simultaneously |
| Solution method | Recursive dynamic solution method |
| Anticipation | GCAM is a dynamic recursive model, meaning that decision-makers do not know the future when making a decision today. After it solves each period, the model then uses the resulting state of the world, including the consequences of decisions made in that period - such as resource depletion, capital stock retirements and installations, and changes to the landscape - and then moves to the next time step and performs the same exercise. For long-lived investments, decision-makers may account for future profit streams, but those estimates would be based on current prices. For some parts of the model, economic agents use prior experience to form expectations based on multi-period experiences. |
Air Pollutant Emissions
Air pollutant emissions (E) such as sulfur dioxide (SOs) and nitrogen oxides (NOx) are modeled as where A is activity level, EF is emissions factor, and EmCtrl is a function that represents decreasing emissions intensity as per-capita income increases:where pcGDP stands for the per-capita GDP, and steepness is an exogenous constant, specific to each technology and pollutant species, that governs the degree to which changes in per-capita GDP will be translated to emissions controls. The purpose here is to capture the general global trend of increasing pollutant controls over time, but does not capture regional and technological heterogeneity. See the documentation's section on air pollution.
