1

Dr. Soheil Rastan, Statistics Canada, P. Eng., MBA. MRIAC

System of Environmental Economic Accounting https://seea.un.org/

Acting Regional Advisor, Economic and Environment Statistics - Statistics Division

United Nations Economic and Social Commission for Asia and the Pacific

soheil.rastan@un.org

soheil.rastan@canada.ca

Regional Inception Workshop on Integrating Statistical Geospatial Data

for Land Accounts and Statistics in Central Asia

Tashkent, Uzbekistan, 14-15 November 2019

City Sustainability Indicator: An Example

2

SEEA-EEA

SEEA-CF

SEEA-AE

SNA

SEEA – EnergySEEA – WaterSEEA – AirSEEA – O2SEEA – GHGSEEA – OceanSEEA – WasteSEEA – ShorelinesSEEA – LandSEEA – City SEEA – ……

Economy ($)

Energy (kJ)

Environment (kg, m3, m2)

SEEA: e3 entropy indicators(construct and variables)

SEEA: Indicators to quantify the unidirectional anthropogenically-induced minimization of

ecosystem entropies

Tipping point (entropy minimization!)

4Plus more …….

5

Beyond the boundary of the GDP, S.R. 2011

GDP

6

Before showcasing what is SEEA Land or SEEA City, etc.

let us start with an easier example

7

Before showcasing what is SEEA Land or SEEA City, etc.

let us start with an easier example

8

Before showcasing what is SEEA Land or SEEA City, etc.

let us start with an easier example

SEEA: Egg

PEC; CEN; CEG; COH; COB; CON; COG; MEB; MEU; MEN; MEG; IPB; IPU; IPN; IPG INVF; INVR INTEX; INTRX

MPG MPSENEIMGIMSFICNNPNGS

PRM1

PRM2

GVAPRM3to PRM8

BS; FC; NP; GS

MPG MPSENEIMGIMSFICNNPNGS

PRM1

INTIM MRGTDE; MRGTRN; MRGTAX

BS; FC; NP; GS

40

%

60

%

% %

11

46

Supply @ P

Use@ P

Sum

= 11

46

Taxes on imported products

Sum of Col

Sum of Col.

Sum

= 68

5

Sum

= 95

5

Sum

= 46

1

Sum

of ro

ws

Eggs-in-Shell, Million $ (@ P), Canada, 2012 BS Supply of Eggs (Animal Production) M

PG

= Eggs in Sh

ell

884 71 187; 4; 0

Total Supply of Eggs at Basic Prices = 884 + 71 = 995Total Supply of Eggs at Purchaser's Prices = 995 + 187 + 4 + 0 = 1146Taxes on imported products is an aggregate for all types of imported products

* 10 million is missing to add up due to “confidentiality”

MP

G = Eggs in

Shell

BS Use of Eggs in Shell

Bakeries and Tortilla Manufacturing = 8

Other Food Manufacturing = 113

Gasoline Stations = 1Hotels = 25

Camps, Room and Boards = 2 Restaurants = 279

Operating Supplies = 22

8; 113; 1; 25; 2; 279; 22 = 450*

PECs “Eggs in Shell” as Food consumed by Residents

in Canada = 649”Eggs in Shell” consumed by Canadians Abroad

= 1”Eggs in Shell” consumed by Non-Residents in

Canada = -6

649; 1; -6

0; -3

44; 0

Total Su

m = 4

61

+ 68

5 = 1

14

6

SEEA Egg (Canada, 2012 estimates)

6.7 billion

28 million m3

3,000 km2

10,300 TJ

$$1.1 billion

40%

60%

430,000 tonnes

= =

≈ 1 x annual energy for 98,000 Canadian households

≈ 8 x the area of the Island of Montreal

≈ 1 x annual water consumptions for 300,000 Canadians

≈ 1 x weight of the Empire State Building

358 cal. → 72 cal. (inedible energy → edible energy)

Energy Edibility Conversion Factor = 5

Ecosystem Sustainability Indicator: Egg

e-CPI-b: Egg

Geospatial data and Statistics at the Egg level

6.7 billion

28 million m3

3,000 km2

10,300 TJ

$$1.1 billion

40%

60%

430,000 tonnes

= =

≈ 1 x annual energy for 98,000 Canadian households

≈ 8 x the area of the Island of Montreal

≈ 1 x annual water consumptions for 300,000 Canadians

≈ 1 x weight of the Empire State Building

14

City Metabolism

City’s Potential Energy ≡

City’s Ecosystem Potential Capacity

Geospatial data and Statistics at the City level

15

Ecosystem Potential Capacity

16

Lower Ecosystem Potential Capacity

potential capacity shelter

17

Ecosystem Potential Capacity

18

Lower Ecosystem Potential Capacity

potential capacity road

19

Ecosystem Potential Capacity

20

Lower Ecosystem Potential Capacity

potential capacity Tourism and recreation

21

Case study: Montreal, Canada

22

3 3 2 1

Examples of land cover classes

1=built up area; 2 = grass land; 3 forestland/wetland

23

Components of a Geospatial Unit Change

• Composition i.e., what is in it

• Structure i.e., what is around it

• Function i.e., what has changed

Δ

Δ

t2 - t1

24

Composition and Structure

The micro scale (the cell) and the macro scale (1 kilometre radius buffer)

25

Input (binary signal) Output

(ternary signal) Logical operation (V ≡ OR, Λ ≡ AND)

Δ Element (2011-2001) cell V buffer cell Λ buffer

ΔE 1, 2, 3, 4, 5, 6

0 0 0

1 0 1

1 1 2

Non Parametric Signal Processing Script

The two togetherOne alone

Cell

Buffer

26

27

The Census Metropolitan Area of Montreal versus the 25-km radius study area

28

Cell A Cell B Cell C

Point ID 13238 5888 566

Cell ID 13421 6019 612

Cell area (km2) 0.0625 0.0625 0.0625

Micro E1 3 3 2

Macro E2 120 119 102

Micro E3 5 4 2

Macro E4 450 504 90

Micro E5 0 0 0

Macro

2001 Elements

E6 2325 1649 1245

Micro E1 1 3 2

Macro E2 106 115 102

Micro E3 10 9 2

Macro E4 1523 684 90

Micro E5 691 418 0

Macro

2011 Elements

E6 6260 4214 1245

Micro ΔE1 -2 0 0

Macro ΔE2 -12 -4 0

Micro ΔE3 5 5 0

Macro ΔE4 1068 175 0

Micro ΔE5 691 418 0

Macro

Difference (2011-2001)

ΔE6 3245 2147 0

Micro ΔE1 1 0 0

Macro ΔE2 1 1 0

Micro ΔE3 1 1 0

Macro ΔE4 1 1 0

Micro ΔE5 1 1 0

Macro

Binary Signals

ΔE6 1 1 0

Micro ΔE1 AND ΔE2 2 0 0

Macro ΔE1 OR ΔE2 1 1 0

Micro ΔE3 AND ΔE4 2 2 0

Macro ΔE3 OR ΔE4 1 1 0

Micro ΔE5 AND ΔE6 2 2 0

Scal

e

Macro

Ternary Signals

ΔE5 OR ΔE6 1 1 0

Likelihood Signal R27 (2,2,2) R18 (1,2,2) R1 (0,0,0)

Intensity Signal High Low No change

27,535 cells

29

30

31

Industrial/commercial developments, case study area, 2001 - 2011Cells R25

32

City Ecosystem Sustainability Indicator: Land Metabolism

Montreal, 2001 - 2011Colour fraction is the area per intensity signals (km2, log10 scale).

Numerical figures are cumulative land area

1716 km2 0 km2

33

The Handbook of Clean Energy Systems: Environmental Sustainability Indicators

John Wiley & Sons, Ltd., UK , 2015

Capacity Building, Integrating and Sharing

Year 2003

Year 2015

Kyrgyzstan

Legend

Kazakhstan.tif

Value

Artificial surfaces

Herbaceous crops

Woody crops

Multiple or layered crops

Grasslands

tree-covered areas

Mangroves

Shrub-covered areas

Shrubs and/or herbaceous vegetation, aquatic or regularly flooded

Sparsely natural vegetated areas

Terrestrial barren land

Permanent snow and glaciers

Inland water bodies

Year 2015Year 2003

Legend

Kazakhstan.tif

Value

Artificial surfaces

Herbaceous crops

Woody crops

Multiple or layered crops

Grasslands

tree-covered areas

Mangroves

Shrub-covered areas

Shrubs and/or herbaceous vegetation, aquatic or regularly flooded

Sparsely natural vegetated areas

Terrestrial barren land

Permanent snow and glaciers

Inland water bodies

Legend

Hot spote.tif

ValueHigh : 137227

Low : 0

Bishkek, Kyrgyzstan

Kyrgyzstan

Legend

Kazakhstan.tif

Value

Artificial surfaces

Herbaceous crops

Woody crops

Multiple or layered crops

Grasslands

tree-covered areas

Mangroves

Shrub-covered areas

Shrubs and/or herbaceous vegetation, aquatic or regularly flooded

Sparsely natural vegetated areas

Terrestrial barren land

Permanent snow and glaciers

Inland water bodies

Year 1995

Year 2015

Uzbekistan

Year 1995 Year 2015

Legend

Kazakhstan.tif

Value

Artificial surfaces

Herbaceous crops

Woody crops

Multiple or layered crops

Grasslands

tree-covered areas

Mangroves

Shrub-covered areas

Shrubs and/or herbaceous vegetation, aquatic or regularly flooded

Sparsely natural vegetated areas

Terrestrial barren land

Permanent snow and glaciers

Inland water bodies

Legend

Hot spote.tif

ValueHigh : 137227

Low : 0

Tashkent, Uzbekistan

Uzbekistan

Year 1995

Year 2015

Turkmenistan

Legend

Kazakhstan.tif

Value

Artificial surfaces

Herbaceous crops

Woody crops

Multiple or layered crops

Grasslands

tree-covered areas

Mangroves

Shrub-covered areas

Shrubs and/or herbaceous vegetation, aquatic or regularly flooded

Sparsely natural vegetated areas

Terrestrial barren land

Permanent snow and glaciers

Inland water bodies

Legend

Hot spote.tif

ValueHigh : 137227

Low : 0

Legend

Kazakhstan.tif

Value

Artificial surfaces

Herbaceous crops

Woody crops

Multiple or layered crops

Grasslands

tree-covered areas

Mangroves

Shrub-covered areas

Shrubs and/or herbaceous vegetation, aquatic or regularly flooded

Sparsely natural vegetated areas

Terrestrial barren land

Permanent snow and glaciers

Inland water bodies

Year 1995 Year 2015

Turkmenistan

Ashgabat, Turkmenistan

Tajikistan

Legend

Kazakhstan.tif

Value

Artificial surfaces

Herbaceous crops

Woody crops

Multiple or layered crops

Grasslands

tree-covered areas

Mangroves

Shrub-covered areas

Shrubs and/or herbaceous vegetation, aquatic or regularly flooded

Sparsely natural vegetated areas

Terrestrial barren land

Permanent snow and glaciers

Inland water bodies

Year 1995

Year 2015

Legend

Kazakhstan.tif

Value

Artificial surfaces

Herbaceous crops

Woody crops

Multiple or layered crops

Grasslands

tree-covered areas

Mangroves

Shrub-covered areas

Shrubs and/or herbaceous vegetation, aquatic or regularly flooded

Sparsely natural vegetated areas

Terrestrial barren land

Permanent snow and glaciers

Inland water bodies

Legend

Hot spote.tif

ValueHigh : 137227

Low : 0

Year1995 Year2015

Tajikistan

Dushanbe, Tajikistan

Afghanistan

Year 1995

Year 2015

Legend

Kazakhstan.tif

Value

Artificial surfaces

Herbaceous crops

Woody crops

Multiple or layered crops

Grasslands

tree-covered areas

Mangroves

Shrub-covered areas

Shrubs and/or herbaceous vegetation, aquatic or regularly flooded

Sparsely natural vegetated areas

Terrestrial barren land

Permanent snow and glaciers

Inland water bodies

Legend

Kazakhstan.tif

Value

Artificial surfaces

Herbaceous crops

Woody crops

Multiple or layered crops

Grasslands

tree-covered areas

Mangroves

Shrub-covered areas

Shrubs and/or herbaceous vegetation, aquatic or regularly flooded

Sparsely natural vegetated areas

Terrestrial barren land

Permanent snow and glaciers

Inland water bodies

Legend

Hot spote.tif

ValueHigh : 137227

Low : 0

Kandahar, Afghanistan

1995 2015

Afghanistan

Year 1995

Year 2015

Armenia

Legend

Kazakhstan.tif

Value

Artificial surfaces

Herbaceous crops

Woody crops

Multiple or layered crops

Grasslands

tree-covered areas

Mangroves

Shrub-covered areas

Shrubs and/or herbaceous vegetation, aquatic or regularly flooded

Sparsely natural vegetated areas

Terrestrial barren land

Permanent snow and glaciers

Inland water bodies

Year 2015Year 1995

Legend

Kazakhstan.tif

Value

Artificial surfaces

Herbaceous crops

Woody crops

Multiple or layered crops

Grasslands

tree-covered areas

Mangroves

Shrub-covered areas

Shrubs and/or herbaceous vegetation, aquatic or regularly flooded

Sparsely natural vegetated areas

Terrestrial barren land

Permanent snow and glaciers

Inland water bodies

Legend

Hot spote.tif

ValueHigh : 137227

Low : 0

Yerevan, Armenia

Armenia

Year 1995

Year 2015

Azerbaijan

Legend

Kazakhstan.tif

Value

Artificial surfaces

Herbaceous crops

Woody crops

Multiple or layered crops

Grasslands

tree-covered areas

Mangroves

Shrub-covered areas

Shrubs and/or herbaceous vegetation, aquatic or regularly flooded

Sparsely natural vegetated areas

Terrestrial barren land

Permanent snow and glaciers

Inland water bodies

Legend

Azerbaijan_Baku_city.tif.tif

Value

Artificial surfaces

Herbaceous crops

Graslands

tree-covered areas

Mangroves

Shrub-covered areas

Sparsely natural vegetated areas

Terrestrial barren land

Inland water bodies

Year 1995 Year 2015

Baku, Azerbaijan

Legend

Hot spote.tif

ValueHigh : 137227

Low : 0

Azerbaijan

Year 1995

Year 2015Kazakhstan

Legend

Kazakhstan.tif

Value

Artificial surfaces

Herbaceous crops

Woody crops

Multiple or layered crops

Grasslands

tree-covered areas

Mangroves

Shrub-covered areas

Shrubs and/or herbaceous vegetation, aquatic or regularly flooded

Sparsely natural vegetated areas

Terrestrial barren land

Permanent snow and glaciers

Inland water bodies

Year 1995

Year 2015

Legend

Kazakhstan_Tselinogradskiy2015.tif.tif

Value

Artificial surfaces

Herbaceous crops

Woody crops

Multiple or layered crops

Graslands

tree-covered areas

Mangroves

Shrub-covered areas

Shrubs and/or herbaceous vegetation, aquatic or regularly flooded

Sparsely natural vegetated areas

Terrestrial barren land

Permanent snow and glaciers

Inland water bodies

Legend

Hot spote.tif

ValueHigh : 137227

Low : 0

Kazakhstan

Nur-Sultan, Kazakhstan

1995 land type(rows)/2015 land type(cols) Ref 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Total 1995

Artificial surfaces 1 705.74 705.74

Herbaceous crops 2 190.20 6177.28 0.96 37.20 0.30 24.56 58.97 0.59 6490.05

Woody crops 3 2.80 2.80

Multiple or layered crops 4 2623.95 3.38 85971.36 847.87 54.92 3.24 1469.42 222.77 25.07 91221.96

Grassland 5 288.06 144.18 1.17 735.88 24936.29 127.34 242.70 139.62 65.07 26680.31

Tree-covered areas 6 0.15 5.22 120.94 122.93 289.17 36.46 25.66 44.99 11.92 657.44

Mangroves 7

Shrub-covered areas 8 4.71 0.89 44438.70 0.59 44444.88

Shrubs and/or herbaceous vegetation 9 2.65 665.52 0.07 668.24

Sparsely natural vegetated areas 10 28.53 108.52 582.52 442.31 1.25 21969.30 112.41 48.45 23293.29

Terrestrial barren land 11 35.66 40.29 547.60 1084.89 4.56 2.94 2649.97 243279.96 244.31 247890.18

Permanent snow and glaciers 12 391.14 391.14

Inland water bodies 13 0.07 1.61 39.34 43.45 15.96 20.95 1.03 4.34 400.91 6004.28 6531.95

Coastal water bodies 14

Total 2015 3877.06 6480.48 3.97 88001.25 27514.94 494.37 44502.30 666.55 26385.94 244259.64 391.14 6400.35 448,978

Example: Land Cover Change Matrix 1995 vs 2015Non-official statistics calculated by ESCAP, QGIS-R script. Figures are in square kilometers.