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CTAP Ex- 1 - Hurricane DRR

M and E Calculation View

Monitoring and Evaluation 2 Output Calculator

Introduction
This tool tracks monitoring and evaluation indicators for output uris. Up to 15 new M and E indicators can be added for each output.

Calculation View Description
This CTA is Example 1 in the Technology Assessment 2 tutorial.v218a

Version: 2.0.4

Feedback About carbon/output/CTAP Ex- 1 - Hurricane DRR/2141223461/me2

Step 1 of 3. Make Selections

M and E Indicators

Indicator 1

Indicator 2

Indicator 3

Indicator 4

Indicator 5

Indicator 6

Indicator 7

Indicator 8

Indicator 9

Indicator 10

Step 2 of 3. Enter M and E Indicators

Relations

Use In Childs?
Overwrite Childs?

More M and E Indicators

Indicator 11

Indicator 12

Indicator 13

Indicator 14

Indicator 15

Score

Step 3 of 3. Save

Method 1. Do you wish to save step 2's calculations? These calculations are viewed by opening this particular calculator addin.

Instructions

Step 1

  • Step 1. Indicators: Enter up to 10 indicators.
  • Step 1. Indicator Name and Description: Name and description for each indicator.
  • Step 1. Indicator Date: Make sure that the benchmark, targets, actual, indicators have distinct dates.
  • Step 1. Distribution Type: The numeric distribution of QT. Refer to the Stock Calculation 1 reference.
  • Step 1. Math Expression:A mathematical expression containing one or more of the Q1 to Q5 variables and/or sibling indicator Q1 to QTM variables. Use strings that identify both the indicator (I1, I2, … In) and the Qx property (Q … QTM), with a period separator between them. Examples include:((I1.Q1 + I1.Q2) * I1.Q3) + I1.Q4)) - (2 * I1.Q5)
  • Step 1. Math Operator Type: Mathematical operation to use with QT. MathTypes include: equalto, lessthan, greaterthan, lessthanorequalto, and greaterthanorequalto. Refer to the Stock Calculation 1 reference for the algorithms.
  • Step 1. Math Type and Math Sub Type: Mathematical algorithm and subalgorithm to use with Distribution Type, QT, QTD1, and QTD2 to solve for QTM, QTL, and QTU. Refer to the Stock Calculation 1 reference for the algorithms.
  • Step 1. QT Amount and Unit: The Unit must be manually entered. The Amount will be the result of the mathematical calculation.
  • Step 1. QTD1 Amount and Unit: First distribution, or shape, parameter for QT.
  • Step 1. QTD2 Amount and Unit: Second distribution, or scale, for QT.
  • Step 1. BaseIO: Base input or output property to update with this indicator's QTM property.
  • Step 1. QTM Amount and Unit: Most Likely Estimate for QT. The Unit must be manually entered. The Amount will be the result of the mathematical algorithm.
  • Step 1. QTL Amount and Unit: Low Estimate or QT. The Unit must be manually entered. The Amount will be the result of the mathematical algorithm.
  • Step 1. QTU Amount and Unit: High Estimate for QT. The Unit must be manually entered. The Amount will be the result of the mathematical algorithm.
  • Step 1. Math Result: TEXT string holding results of calculation.

Step 2

  • Step 2. Use Same Calculator Pack In Descendants?: True to insert or update this same calculator in children.
  • Step 2. Overwrite Descendants?: True to insert or update all of the attributes of this same calculator in all children. False only updates children attributes that are controlled by the developer of this calculator (i.e. version, stylehsheet name, relatedcalculatorstype ...)
  • Step 2. What If Tag Name: Instructional videos explaining the use of what-if scenario calculations should be viewed before changing or using this parameter.
  • Step 2. Related Calculators Type: When the Use Same Calculator Pack in Descendant is true, uses this value to determine which descendant calculator to update. Inserts a new descendant when no descendant has this same name. Updates the descendant that has this same name.
  • Step 2. Indicators: Enter up to 5 indicators.
  • Step 2. Target Type: Used with Progress analyzers to identify benchmark and actual indicators.
  • Step 2. Altern Type: Used with Change by Alternative analyzers to identify alternatives to compare.
  • Step 2. Score Math Expression: A mathematical expression containing one or more of the children indicator Q1 to QTM variables. Use strings that identify both the indicator (I1, I2, … In) and the Qx property (Q … QTM), with a period separator between them. Examples include:((I1.QTM + I2.QTM) * I3.Q3) + I4.QTM)) - (2 * I5.QTM)
  • Step 2. Score Total and Unit: The Unit must be manually entered. The Amount will be the result of the Math Expression calculation.
  • Step 2. D1 Amount and Unit: First distribution variable for Score.
  • Step 2. D2 Amount and Unit: Second distribution for Score.
  • Step 2. Distribution Type: The numeric distribution of Score. Refer to the Stock Calculation 1 reference.
  • Step 2. Math Type and Math Sub Type: Mathematical algorithm and subalgorithm to use with Distribution Type, Score, ScoreD1, and ScoreD2 to solve for ScoreM, ScoreL, and ScoreU. Refer to the Stock Calculation 1 reference for the algorithms.
  • Step 2. Most Likely, Low, High, Amounts and Units: Results of Distribution Type and Math Type calculations.
  • Step 2. Iterations: Number of iterations to use when drawing random number samples for some algorithms.
  • Step 2. Confidence Interval: Level of confidence interval to use when reporting all Score and Indicator high and low amounts. Should be an integer such as 95, 90, or 40.
  • Step 2. Random Seed: Any positive integer, except 0, will result in the same set of random variables being used each time a calculation is run.
  • Step 2. BaseIO: Base input or output property to update with the Score Most Likely property.

References

  • Refer to the M and E Introduction and Resource Stock Calculation references.

Current view of document
CTAP Ex- 1 - Hurricane DRR
Output Group
CTAP Output Examples
Output
CTAP Ex- 1 - Hurricane DRR
Name (N) Label Date Rel Label Math Type Dist Type Base IO or Obs Math Operator Math Sub Type
Q1 Amount Q1 Unit Q2 Amount Q2 Unit Q3 Amount Q3 Unit Q4 Amount Q4 Unit Q5 Amount Q5 Unit
QT Amount QT Unit QT D1 Amount QT D2 Amount QT Most Amount QT Most Unit QT Low Amount QT Low Unit QT High Amount QT High Unit
Disaster Score S0 11/23/2016 none algorithm1 normal none equalto subalgorithm1
12.0432 most likely bcr 12.0237 3.0000 11.9752 most likely 11.8160 lower 90 % ci 12.1344 upper 90 % ci
I6.QTM
Hazard Distribution 1A 11/23/2016 none algorithm1 none equalto subalgorithm9
104.8302 100year 78.6227 50year 52.4151 25year 36.6906 10year 26.2076 5year
49.8750 wind spped 49.8750 4.9875 49.7943 mean wind speed mph 49.5297 lower 90 % ci 50.0590 upper 90 % ci
Each reach is described by a probability distribution (QTs) defined by the event probability (1, 2, 4, 10, 20 percent) and the associate quantity of the hazard (the matrix numbers).
I1.Q1.distribtype + I1.Q2.100year + I1.Q3. 50year + I1.Q4.25year + I1.Q5.10year + I1.Q6.5year
Exposure Distribution 2A 11/23/2016 none algorithm1 none none equalto subalgorithm9
2,196,441.8938 location 1 total value 2,081,627.8856 location 2 total value 0.0000 none 0.0000 none 0.0000 none
4,278,069.7794 1, 2 totals 0.0000 0.0000 4,278,069.7794 drr all locations 4,255,331.5981 lower 90 % ci 4,300,807.9607 upper 90 % ci
The total value of each asset type is described by a probability distribution (QTs) calculated from the price (p1) and quantity (q1) of the asset.
I2.Q1.distribtype + I2.Q2.QT + I2.Q3.QTUnit + I2.Q4.QTD1+ I2.Q5.QTD1Unit + I2.Q6.QTD2 + I2.Q7.QTD2Unit + I2.Q8.normalization + I2.Q9.weight + I2.Q10.quantity
Vulnerability Distribution 3A 11/23/2016 none algorithm1 none none equalto subalgorithm9
5.2416 5year 38.7872 10year 135.7550 25year 203.3705 50year 273.6069 100year
0.0000 total percent damage 0.0000 0.0000 17.1607 total percent damage 17.0695 lower 90 % ci 17.2519 upper 90 % ci
The damage percent for each asset type is described by a probability distribution (QTs) defined by the flood depth (0.5, 1.0, 1.5, 2.0, 2.5) and the percent of asset damage (the matrix numbers).
I3.Q1.distribtype + I3.Q2.5year + I3.Q3.10year + I3.Q4.25year + I3.Q5.50year + I3.Q6.100year
Loss EP Distribution 4A 01/01/2005 none algorithm1 none none equalto subalgorithm9
5,516.0643 5year 59,925.9235 10year 284,872.5233 25year 521,089.3660 50year 926,494.1342 100year
0.0000 total avg ann damages 0.0000 0.0000 38,177.4348 total avg ann damages 37,783.9036 lower 90 % ci 38,573.1035 upper 90 % ci
The damages for each asset type is described by a probability distribution (QTs) defined by the event probability (20, 10, 4, 2, 1) and the total damages (the matrix numbers).
I4.Q1.distribtype + I4.Q2.5year + I4.Q3.10year + I4.Q4.25year + I4.Q5.50year + I4.Q6.100year
Project Costs 5A 01/01/2005 none algorithm1 none none equalto subalgorithm9
0.0000 none 0.0000 none 0.0000 none 0.0000 none 0.0000 none
0.0000 mean avg annual costs 0.0000 0.0000 3,023.6317 mean avg annual costs 3,007.5609 lower 90 % ci 3,039.7026 upper 90 % ci
The cost for each project alternative is described as the probability of average annual costs (QTs).
I5.Q1.installcost + I5.Q2.installdistrib + I5.Q3.omcost+ I5.Q4.omdistrib + I5.Q5.isprojectcost
Benefit Cost Analysis 6A 11/23/2016 none algorithm1 none none equalto subalgorithm9
538,070.6497 base damage 232.3824 base cost 386,118.2691 AC1A_B damage 12,849.6552 AC1A_B cost 0.0000 none
139,335.1079 net benefits 0.0000 0.0000 12.0432 2_QTM_0.05_25, bcr 11.9977 lower 90 % ci 12.1065 upper 90 % ci
The Math Results from Indicator 4 define average annual benefits and the Math Results from Indicator 5 defines average annual costs. Benefits are defined as the reduction in damages of each project alternative in comparison to the baseline.
I6.Q1.distribtype + I6.Q2.100year + I6.Q3.50year + I6.Q4.25year + I6.Q5.10year + I6.Q6.5year
Cost Effectiveness Analysis 7A 10/02/2015 none algorithm1 none none equalto subalgorithm9
38,177.4348 base damage 195.2062 base cost 27,396.0400 AC1A_B damage 12,008.7980 AC1A_B cost 0.0000 none
1.0957 net benefits 0.0000 0.0000 1.0957 2_QTM_0.12_10, cer 1.0999 lower 90 % ci 1.0900 upper 90 % ci
The URL dataset define average annual benefits from non monetary disaster loss reductions and the Math Results from Indicator 5 defines average annual costs. Benefits are defined as the reduction in damages of each project alternative in comparison to the
I7.Q1.distribtype + I7.Q2.100year + I7.Q3.50year + I7.Q4.25year + I7.Q5.10year + I7.Q6.5year
Output Series: CTAP Ex- 1 - Hurricane DRR
Name (N) Label Date Rel Label Math Type Dist Type Base IO or Obs Math Operator Math Sub Type
Q1 Amount Q1 Unit Q2 Amount Q2 Unit Q3 Amount Q3 Unit Q4 Amount Q4 Unit Q5 Amount Q5 Unit
QT Amount QT Unit QT D1 Amount QT D2 Amount QT Most Amount QT Most Unit QT Low Amount QT Low Unit QT High Amount QT High Unit
Disaster Score S0 11/23/2016 none algorithm1 normal none equalto subalgorithm1
12.0432 most likely bcr 12.0237 3.0000 11.9752 most likely 11.8160 lower 90 % ci 12.1344 upper 90 % ci
I6.QTM
Hazard Distribution 1A 11/23/2016 none algorithm1 none equalto subalgorithm9
104.8302 100year 78.6227 50year 52.4151 25year 36.6906 10year 26.2076 5year
49.8750 wind spped 49.8750 4.9875 49.7943 mean wind speed mph 49.5297 lower 90 % ci 50.0590 upper 90 % ci
Each reach is described by a probability distribution (QTs) defined by the event probability (1, 2, 4, 10, 20 percent) and the associate quantity of the hazard (the matrix numbers).
I1.Q1.distribtype + I1.Q2.100year + I1.Q3. 50year + I1.Q4.25year + I1.Q5.10year + I1.Q6.5year
Exposure Distribution 2A 11/23/2016 none algorithm1 none none equalto subalgorithm9
2,196,441.8938 location 1 total value 2,081,627.8856 location 2 total value 0.0000 none 0.0000 none 0.0000 none
4,278,069.7794 1, 2 totals 0.0000 0.0000 4,278,069.7794 drr all locations 4,255,331.5981 lower 90 % ci 4,300,807.9607 upper 90 % ci
The total value of each asset type is described by a probability distribution (QTs) calculated from the price (p1) and quantity (q1) of the asset.
I2.Q1.distribtype + I2.Q2.QT + I2.Q3.QTUnit + I2.Q4.QTD1+ I2.Q5.QTD1Unit + I2.Q6.QTD2 + I2.Q7.QTD2Unit + I2.Q8.normalization + I2.Q9.weight + I2.Q10.quantity
Vulnerability Distribution 3A 11/23/2016 none algorithm1 none none equalto subalgorithm9
5.2416 5year 38.7872 10year 135.7550 25year 203.3705 50year 273.6069 100year
0.0000 total percent damage 0.0000 0.0000 17.1607 total percent damage 17.0695 lower 90 % ci 17.2519 upper 90 % ci
The damage percent for each asset type is described by a probability distribution (QTs) defined by the flood depth (0.5, 1.0, 1.5, 2.0, 2.5) and the percent of asset damage (the matrix numbers).
I3.Q1.distribtype + I3.Q2.5year + I3.Q3.10year + I3.Q4.25year + I3.Q5.50year + I3.Q6.100year
Loss EP Distribution 4A 01/01/2005 none algorithm1 none none equalto subalgorithm9
5,516.0643 5year 59,925.9235 10year 284,872.5233 25year 521,089.3660 50year 926,494.1342 100year
0.0000 total avg ann damages 0.0000 0.0000 38,177.4348 total avg ann damages 37,783.9036 lower 90 % ci 38,573.1035 upper 90 % ci
The damages for each asset type is described by a probability distribution (QTs) defined by the event probability (20, 10, 4, 2, 1) and the total damages (the matrix numbers).
I4.Q1.distribtype + I4.Q2.5year + I4.Q3.10year + I4.Q4.25year + I4.Q5.50year + I4.Q6.100year
Project Costs 5A 01/01/2005 none algorithm1 none none equalto subalgorithm9
0.0000 none 0.0000 none 0.0000 none 0.0000 none 0.0000 none
0.0000 mean avg annual costs 0.0000 0.0000 3,023.6317 mean avg annual costs 3,007.5609 lower 90 % ci 3,039.7026 upper 90 % ci
The cost for each project alternative is described as the probability of average annual costs (QTs).
I5.Q1.installcost + I5.Q2.installdistrib + I5.Q3.omcost+ I5.Q4.omdistrib + I5.Q5.isprojectcost
Benefit Cost Analysis 6A 11/23/2016 none algorithm1 none none equalto subalgorithm9
538,070.6497 base damage 232.3824 base cost 386,118.2691 AC1A_B damage 12,849.6552 AC1A_B cost 0.0000 none
139,335.1079 net benefits 0.0000 0.0000 12.0432 2_QTM_0.05_25, bcr 11.9977 lower 90 % ci 12.1065 upper 90 % ci
The Math Results from Indicator 4 define average annual benefits and the Math Results from Indicator 5 defines average annual costs. Benefits are defined as the reduction in damages of each project alternative in comparison to the baseline.
I6.Q1.distribtype + I6.Q2.100year + I6.Q3.50year + I6.Q4.25year + I6.Q5.10year + I6.Q6.5year
Cost Effectiveness Analysis 7A 10/02/2015 none algorithm1 none none equalto subalgorithm9
38,177.4348 base damage 195.2062 base cost 27,396.0400 AC1A_B damage 12,008.7980 AC1A_B cost 0.0000 none
1.0957 net benefits 0.0000 0.0000 1.0957 2_QTM_0.12_10, cer 1.0999 lower 90 % ci 1.0900 upper 90 % ci
The URL dataset define average annual benefits from non monetary disaster loss reductions and the Math Results from Indicator 5 defines average annual costs. Benefits are defined as the reduction in damages of each project alternative in comparison to the
I7.Q1.distribtype + I7.Q2.100year + I7.Q3.50year + I7.Q4.25year + I7.Q5.10year + I7.Q6.5year
Dataset: CTAP Ex- 1 - Hurricane DRR IRI This output is used to support a CTAP tutorial for hurricane Disaster Risk Reductions in St. Lucia, Caribbean

Search IRIs:
https://www.devtreks.org/greentreks/linkedviews/carbon/output/CTAP Ex- 1 - Hurricane DRR/2141223461/me2









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