Urban Tool - Forecast Food Demand Maria Caterina Bramati Sapienza - - PowerPoint PPT Presentation

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Urban Tool - Forecast Food Demand Maria Caterina Bramati Sapienza - - PowerPoint PPT Presentation

Jan 19, 2024 271 likes •680 views

Urban Tool - Forecast Food Demand Maria Caterina Bramati Sapienza University of Rome mariacaterina.bramati@uniroma1.it 1 Some 2014 Key Figures 54% worlds population resides in Urban areas Most urbanized regions: North America (82%

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Urban Tool - Forecast Food Demand

Maria Caterina Bramati Sapienza University of Rome

mariacaterina.bramati@uniroma1.it

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Some 2014 Key Figures

  • 54% world’s population resides in Urban areas
  • Most urbanized regions: North America (82% urban population),

Latin America (80% urban population)

  • Least urbanized regions: Africa (40%), Asia (48%)
  • Rapid growth of urban population foreseen by 2050,

90% of the increase concentrated in Africa and Asia

  • 28 mega-cities with > 10million inhabitants, by 2030 the world is

projected to have 41 mega-cities. [World Urbanization Prospects - 2014 Revision]

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Defined on the basis of 5 dimensions: 1. Economic growth (productivity, income, employment) 2. Infrastructure (water, sanitation, road network, ICT…) 3. Social services (education, health, recreation, safety…) 4. Reduction of Poverty and Inequalities 5. Environmental protection and preservation *UN-Habitat State of the World’s cities 2012/2013

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Urban prosperity*(1)

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Chronic inequalities and mass poverty in cities due to:

  • Insufficient infrastructure
  • Poor public services
  • Inadequate connectivity
  • Poor governance
  • Fragile institutions

*UN-Habitat State of the World’s cities 2012/2013

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Urban prosperity*(2)

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“…sustainable development challenges will be increasingly concentrated in cities, particularly in the lower-middle-income countries where the pace of urbanization is fastest…” “…rapid and unplanned growth threatens sustainable development when the necessary infrastructure is not developed

  • r when policies are not implemented to ensure that the benefits
  • f city life are equitably shared.”

[World Urbanization Prospects - 2014 Revision]

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AIM of the TOOL

To raise the awareness of policy makers about the future ROAD Infrastructure need through the

  • 1. Estimation of the food demand in cities and of

the number of daily truck journeys needed to supply cities.

  • 2. Projection of future needs of Road Infrastructure

according of various scenarios of urban growth.

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  • Main assumptions about the demand function
  • Estimation and Scenario Building functions
  • Case Studies
  • Advanced users
  • Open issues
  • Floor Discussion

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OUTLINE

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  • Log-linear food item demand function
  • estimated for 3 income groups (low-mid-high)
  • Mean for transportation: 10-ton truck
  • Food item demand is function of
  • market prices (including substitute and

complements food items),

  • household income
  • household size

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The Econometric Model:

Main assumptions

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Parameters are obtained after LS estimation using LSMS country household data. They are

  • By geographical area
  • By food item
  • Set by default, unless the user decides otherwise

Parameters:

  • Price elasticities to food consumption
  • Income elasticities
  • Household Size coefficient

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The Econometric Model:

Main parameters

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Data used: household food consumption in urban areas of

  • Brazil
  • Tanzania
  • Malawi
  • Ethiopia
  • Iraq
  • Tajikstan
  • Bulgaria

→ timeliness → geographical areas

However…

(some issues about default parameters)

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URBAN TOOL

An interface running in Excel

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URBAN TOOL

Functions

  • 1. Estimate aggregate urban food demand by single

food item, time horizon, geographical area of cities and different income-level of city inhabitants.

  • 2. Forecast aggregate food demand and transport

needs as number of daily truck journeys for a given nutritional composition.

  • 3. Assessment of current capacity to face future

demand using scenarios related to population, inflation and income growth rates.

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URBAN TOOL

Reporting

  • 1. Output from estimation (a separate sheet with

computation is generated).

  • 2. Graphs of food item and aggregate demand for

each of the income groups.

  • 3. Pivot Tables with the aggregate food demand and

transport needs as number of daily truck journeys.  Possible to save reports creating new .xls files

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URBAN TOOL

Remarks

  • Stepwise procedure: steps are numbered in

increasing order. Skipping steps will generate errors.

  • Compulsory actions (marked by *): input values

related to city features.

  • Free choice of parameters: the user can choose

whether using default parameters or enter other coefficients.

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URBAN TOOL

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READY? Let’s go for a drive!

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URBAN TOOL

Case Study 1: Lilongwe (Malawi)

AIM: Estimate daily demand of cereals and rice

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URBAN TOOL

Case Study 1: Lilongwe (Malawi)

AIM: Estimate daily demand of cereals and rice  Use function 1 Input needed:

  • Lilongwe inhabitants
  • Average household size
  • Per capita daily income $ US
  • Market prices/kg $ US

[if possible by income group]

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URBAN TOOL

Case Study 1: Lilongwe (Malawi)

AIM: Estimate daily demand of cereals and rice  KEY Figures (2014)

  • Lilongwe inhabitants : 867469
  • Average household size: 4.45
  • Per capita daily income: 2.41 $ US
  • Market price of Cereals: 0.64 $ US/kg
  • Market price of Rice: 1.05 $ US/kg

[Source: The WorldBank, LSMS]

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URBAN TOOL

Case Study 1: Lilongwe (Malawi)

AIM: Estimate daily demand of cereals and rice  By income group

[Source: The WorldBank, LSMS] low middle high inhabitants 650601.8 173493.8 43373.45 average hh size 4.45 4.45 4.45 per capita daily income 2 8 82

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URBAN TOOL

Case Study 1: Lilongwe (Malawi)

AIM: Estimate daily demand of cereals and rice  Change Elasticities for Cereals (Set parameters)

[Source: Maganga et al. (2014)] New Default Income 1.062 2.718 Price 0.824 0.607 Houshold size 1.22 1.22

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URBAN TOOL

Case Study 1: Lilongwe (Malawi)

AIM: Estimate daily demand of cereals and rice  Maize daily consumption in Lilongwe (tons)

[Source: FAO (2014)] 2003 2014 Maize 316.09 468.43

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URBAN TOOL

Case Study 2: São Paulo

São Paulo is the 7th most populous city in the world defined according to the concept of city proper (an urban locality without its suburbs). The cities ahead of it in order of population are: Shanghai, Karachi, Istanbul, Mumbai, Beijing and Moscow. Source: IBGE 2010 Census data

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URBAN TOOL

Case Study 2: São Paulo

Transport

  • The average traffic jams on Friday evenings is 180km (112

miles) and as long as 295km (183 miles) on bad days according to local traffic engineers.

  • Commuters are stuck in traffic hold-ups for an average of

two and a half hours daily.

  • Despite the traffic jams São Paulo continues to experience

ever increasing traffic circulation and surpassed 7m vehicles in March 2011.

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URBAN TOOL

Case Study 2: São Paulo

AIM: Estimate daily aggregate food demand  Use function 2 Input needed:

  • São Paulo inhabitants
  • Average household size
  • Per capita daily income
  • Food market price for each item
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URBAN TOOL

Case Study 2: São Paulo

AIM: Estimate daily aggregate food demand  KEY Figures (2014)

  • São Paulo inhabitants : 20830857
  • Average household size: 3.58
  • Per capita daily income: 5.73 $ US

[Source: The WorldBank, LSMS]

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Case Study 2: São Paulo

AIM: Estimate daily aggregate food demand  2014 Food market prices

Item Prices $ US/kg Cereals 1.40 Rice 1.16 Roots and Tubers 1.18 Meats and Meat Products 5.62 Fish 18.00 Dairy Products 0.90 Fruits and Vegetables 1.56 Other Food 1.66

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URBAN TOOL

Case Study 3: Lima

In metropolitan Lima, 7% of the population lives in a tugurio (inner-city barrio) and 47% in a squatter

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URBAN TOOL

Case Study 3: Lima

AIM: Estimate daily aggregate food demand  Use function 2 and change default dietary composition Input needed:

  • Lima inhabitants
  • Average household size
  • Per capita daily income

[by income group]

  • Food market price for each item
  • Dietary composition
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URBAN TOOL

Case Study 3: Lima

 2014 Figures by income group

[Source: The WorldBank, LSMS] low middle high inhabitants 3888828 4861035.5 972207.1 average hh size 5.5 4.7 3.9 per capita daily income 2 10 30

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Case Study 3: Lima

AIM: Estimate daily aggregate food demand  2014 Food market prices and dietary composition

Item Prices $ US/kg Low % Middle % High % Cereals 2.6 4.9 4.6 3.2 Rice 1.03 15.9 13.0 10.4 Roots and Tubers 0.88 9.5 8.9 7.7 Meat 5.72 26.0 30.1 35.7 Fish 7.34 7.2 6.4 6.4 Dairy Products 1.22 13.1 15.0 15.8 Fruits & Vegetables 1.38 15.2 16.1 16.4 Other Food 1.80 8.3 5.6 4.4

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URBAN TOOL

Case Study 4: Dar-Es-Salaam

Dar es Salaam is on track to become Africa's fastest-growing urban center.

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URBAN TOOL

Case Study 4: Dar-Es-Salaam

  • Dar es Salaam is on track to

become Africa's fastest- growing urban center.

  • New York City added

roughly 4 million residents in the past 100 years. Dar es Salaam will add 21 million over a similar span.

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Case Study 4: Dar-Es-Salaam

The city’s road network totals about 1,950 km in length, of which 1120 km (less than 60%) is paved, and is inadequate to satisfy its population density, spatial expansion and transportation needs. Dar es Salaam hosts about 52% of Tanzania’s vehicl es, and has a traffic density growth rate of over 6.3% per year. (JICA, 1995; Kanyama et al., 2004).

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URBAN TOOL

Case Study 4: Dar-Es-Salaam

AIM: Project future aggregate food demand under scenarios  Use function 3  First estimate aggregate demand Input needed:

  • Inhabitants
  • Average household size
  • Per capita daily income

[by income group]

  • Food market price for each item
  • Scenarios on population growth, income and inflation
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URBAN TOOL

Case Study 4: Dar-Es-Salaam

AIM: Estimate daily aggregate food demand  Use function 2 2014 Figures by income group

[Source: The WorldBank, LSMS] low middle high inhabitants 3386702 1209536 241907 average hh size 5.8 4 2.9 per capita daily income 1 3.67 136

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Case Study 4: Dar-Es-Salaam

 2014 Food market prices

Item Prices $ US/kg Cereals 0.78 Rice 1.15 Roots and Tubers 1.02 Meat 8.01 Fish 7.88 Dairy Products 6.5 Fruits & Vegetables 2.13 Other Food 2.06

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Forecast window: 15 years

  • Population growth: 129% (yearly

8%) for each group

  • Income growth: 55% in middle

classes, 10% low, 20% in high

  • Inflation rate: 2.2%

Case Study 4: Dar-Es-Salaam

  • Scenarios Building-
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WARNING!

Scenario Building works only agter having estimated the aggregate daily demand (do not put any time options in Function 2!)

Case Study 4: Dar-Es-Salaam

  • Scenarios Building-
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THANKS FOR YOUR ATTENTION!

mariacaterina.bramati@uniroma1.it