City-level climate policy model

This page is a assessment. The page identifier is Op_en5811
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Answer

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Add a legend for your diagram.

Rationale

Dependencies

This is a generic table format and assessment structure for most inputs of the model. This is based on File:UrgencheConceptualModel.xlsx

We need too tables, one with all issues listed (with estimates where there is direct data), and another for equations that connect issues. The assessment has seven steps:

• Policy target
• Action on
• Primary impact on
• End use of energy
• Emission
• Time budget
• Exposure
• Policy outcome

This table contains the issues:

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City-level climate policy model(-)

Obs	Step	Topic	Issue	Unit	Result	Description
1	Policy target	Urban area	Urban density	m2/km2		floor space
2	Policy target	Urban area	Urban plan/zoning
3	Policy target	Urban area	Street network	lane-km(/km2)
4	Policy target	Urban area	Rail network	rail-km(/km2)
5	Policy target	Urban area	Walk/bike network	km(/km2)
6	Policy target	Transport	Local person transport	total person-km/a
7	Policy target	Transport	(intercity personal transport)
8	Policy target	Transport	Local transport of goods
9	Policy target	Transport	(long range goods transport)
10	Policy target	Transport	(street) transport electrification	% person-km
11	Policy target	Buildings (incl activities in)	Residental small houses	#buildings, m3		tot m3
12	Policy target	Buildings (incl activities in)	Residental appartment buildings	#residences, m3		tot m3
13	Policy target	Buildings (incl activities in)	Commercial (incl office) buildings	#buildings, m3		tot m3
14	Policy target	Buildings (incl activities in)	Recreational/cultural buildings	#buildings, m3		tot m3
15	Policy target	Buildings (incl activities in)	Educational buildings	#buildings, m3		tot m3
16	Policy target	Buildings (incl activities in)	Healthcare/institutional buildings	#buildings, m3		tot m3
17	Policy target	Buildings (incl activities in)	Other (incl. Industrial) buildings)	#buildings, m3		tot m3
18	Action on	Urban develompent	population density	#/km2		population based distribution
19	Action on	Urban develompent	land use efficiency	m2/km2		floor space areal distribution
20	Action on	Urban develompent	pedestrian zones	% built area
21	Action on	Urban develompent	walk&bike for exercise	person-km/a		estimate
22	Action on	Urban develompent	parks, waters, green corridors	%area within built zone
23	Action on	Urban develompent	local [re]forestation	m3/a		net growth
24	Action on	Transport environment	walk&bike for transport	km/a	100
25	Action on	Transport environment	bus transport	km/a	10
26	Action on	Transport environment	tram/trolley transport	km/a	5
27	Action on	Transport environment	subway&train transport	km/a	1
28	Action on	Transport environment	automobiler transport	km/a	100
29	Action on	Transport environment	scooter transport	km/a	100
30	Action on	Transport environment	bicycle parking downtown
31	Action on	Transport environment	auto&scooter parking at rail stations
32	Action on	Transport environment	auto&scooter parking downtown

The connections between items is created using this kind of a table (here a wiki table format is used, but actually it should be a data table on a page called City-level climate policy model/equations). Proximity to work and school is used as an example. All causal dependencies are interpreted in the following way, where each equation defines one child:

Failed to parse (Missing <code>texvc</code> executable. Please see math/README to configure.): Child = \Sigma Parent_i * Coefficient_i

Dependencies of issues

Equation	Step	Topic	Issue	Role	Coefficient	Description
1	Impact on	Urban environment functionality	Proximity to work and school	Child
1	Action on	Urban development	Population density	Parent	1
1	Action on	Urban development	Land use efficiency	Parent	2.2
1	Policy target	Urban area	Urban plan/zoning	Parent	3.6

Formula

+ Show code- Hide code library(OpasnetUtils) library(xtable) library(ggplot2) test <- 0 issues <- op_baseGetData("opasnet_base", "Op_en5811") me <- "Urban area" formula <- function(dependencies, me, ...) { data <- issues[issues$Topic == me, ] return(data) } ova <- new("ovariable", dependencies = data.frame(Name = "test"), formula = formula ) ova EvalOutput(ova, me = "Transport") equations <- tidy(op_baseGetData("opasnet_base", "Op_en5811"), objname = "equations") # This should be the page "City-level climate policy model/equations" but it does not exist yet. equations ### This formula creates a causal model with linear dependencies between ovariables Policy.target, Action.on, ... # The dependencies are defined in a data table equations. formula <- function(dependencies, me, ...) { ComputeDependencies(dependencies, ...) # Find coefficients of equations that are relevant for this ovariable. # Take those equations that are defining the object at hand. coefficients <- equations[equations$Equation == equations[equations$Role == "Child" & equations$Step == me, "Equation"], ] coefficients <- new("ovariable", name = "coefficients", data = coefficients ) out <- data.frame() for(i in dependencies$Name) { out <- rbind(out, get(i)@output * coefficients) } out <- tapply(out$Result, out["Equation"], sum) return(out) } # Define all ovariables needed. issues Policy.targets <- new("ovariable", name = "Policy.targets", data = tidy(issues[issues$Step == "Policy target" , ], objname = "Policy.target") ) Action.on <- new("ovariable", name = "Action.on", data = tidy(issues[issues$Step == "Action on" , ], objname = "Action.on"), dependencies = data.frame(Name = c( "equation", "Policy.target" )), formula = formula ) Primary.impact.on <- new("ovariable", name = "Primary.impact.on", data = tidy(issues[issues$Step == "Primary impact on" , ], objname = "Primary impact on"), dependencies = data.frame(Name = c( "equation", "Policy.target", "Action.on" )), formula = formula ) End.use.of.energy <- new("ovariable", name = "End.use.of.energy", data = tidy(issues[issues$Step == "End use of energy" , ], objname = "End.use.of.energy"), dependencies = data.frame(Name = c( "equation", "Policy.target", "Action.on", "Primary.impact.on" )), formula = formula ) Emission <- new("ovariable", name = "Emission", data = tidy(issues[issues$Step == "Emission" , ], objname = "Emission"), dependencies = data.frame(Name = c( "equation", "Policy.target", "Action.on", "Primary.impact.on", "End.use.of.energy" )), formula = formula ) Time.budget <- new("ovariable", name = "Time.budget", data = tidy(issues[issues$Step == "Time budget" , ], objname = "Time.budget"), dependencies = data.frame(Name = c( "equation", "Policy.target", "Action.on", "Primary.impact.on", "End.use.of.energy", "Emission" )), formula = formula ) Exposure <- new("ovariable", name = "Exposure", data = tidy(issues[issues$Step == "Exposure" , ], objname = "Exposure"), dependencies = data.frame(Name = c( "equation", "Policy.target", "Action.on", "Primary.impact.on", "End.use.of.energy", "Emission", "Time.budget" )), formula = formula ) Policy.outcome <- new("ovariable", name = "Policy.outcome", data = tidy(issues[issues$Step == "Policy.outcome" , ], objname = "Policy.outcome"), dependencies = data.frame(Name = c( "equation", "Policy.target", "Action.on", "Primary.impact.on", "End.use.of.energy", "Emission", "Time.budget", "Exposure", "Policy.outcome" )), formula = formula ) # Evaluate all ovariables, starting from upstream. EvalOutput(Policy.target, me = "Policy target") EvalOutput(Action.on, me = "Action on") EvalOutput(Primary.impact.on, me = "Primary.impact.on") EvalOutput(End.use.of.energy, me = "End use of energy") EvalOutput(Emission, me = "Emission") EvalOutput(Time.budget, me = "Time.budget") EvalOutput(Exposure, me = "Exposure") EvalOutput(Policy.outcome, me = "Policy.outcome")

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