Urban Growth 2 Growth in Electricity Use (2001-2011) 3 (Kennedy - - PowerPoint PPT Presentation

Estimating the Energy Use and CO2 Emissions of the World’s Cities

Shweta Singh1,2 and Chris Kennedy3

1. Agricultural & Biological Engineering, Purdue University, USA

• 2. Environmental & Ecological Engineering, Purdue University, USA
• 3. Department of Civil Engineering, University of Toronto, Canada

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International Renewable Energy Agency Roundtable Renewable Energy Deployment in Cities, 29th October 2015 , Singapore

Urban Growth

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Growth in Electricity Use (2001-2011)

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(Kennedy et al. 2015)

Modeling Urban Energy Consumption

 How would the future urban growth impact energy consumption

and related emissions at global scale ?  Objectives  Develop a tool for urban energy consumption estimation.  Application of tool for estimation of global scale impact.

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22 City Data Set

 Data on Electricity (MWh/cap), Heating (GJ/cap) and Transportation (GJ/cap).

Asia

Bangkok North/South America Denver Shanghai Los Angeles Beijing New York City Jakarta Toronto Amman Chicago Manila Buenos Aires Tianjin Sao Paulo Europe Barcelona Rio de Janeiro Paris-IDF Geneva Africa Dar es Salaam London Cape Town Prague

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Regression Model for Urban Energy Use

 Energy Consumption Categories

 Electricity (MWh/cap)  Heating (Gj/cap)  Transportation (GJ/cap)

 Energy Consumption Drivers

 Urban Density

 Per capita space – Inv-Density (ha/cap)

 Climate (Heating Degree Days or HDD and Cooling Degree Days or CDD)

 HDD

 Gross Domestic Product (GDP)

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Regression Models

Ref : Singh and Kennedy, Env Pollution Sp. Issue, 2015

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Application at Global Scale

Test on 3646 City Data Set

• No. of Asian Cities : 1783 (approx. 50 %)

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Data for 3646 Global Cities

 Lincoln Institute of Land Policy, Cambridge, MA

 Primary focus on land policy, taxation.  Work on urban expansion.  Shlomo Angel et al.  Working Paper : “A Planet of Cities : Urban Land Cover of Estimates and Projections for All Countries, 2000-2050”  Metropolitan Agglomerations with Populations > 100,000 in 2000  MOD500 : Satellite Based Map of Urban Land Cover  Data  Density : Based on the empirical studies  Population : UN Based projection  Missing Data for Energy Estimation : HDD (Temperature, Weather)

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Estimating HDD : HDDProxy

 Heating Degree Days estimated based on weather data.  Assumption : Heating is required below 18 deg C.  HDD Estimated (HDDProxy) 𝐼𝐸𝐸𝑄𝑠𝑝𝑦𝑧 =

𝑗=1 12

𝑈

𝑏𝑤𝑕 − 18

× 𝑂𝑝. 𝑝𝑔 𝑒𝑏𝑧𝑡 𝑗𝑜 𝑓𝑏𝑑ℎ 𝑛𝑝𝑜𝑢ℎ

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Determining Tavg for Urban Areas

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Climate Data Extracted from Global Historical Climate Network (University of Delaware)  GIS gridded monthly air temperature data  Great Circle Distance method for surface distance calculations.  Tavg = mean of stations < 25 miles from urban areas

Is the model applicable for Global Estimation ?

 Statistical Testing for applicability

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Extrapolation Check for Global City Data

 Based on HDD, 11 %

• f HDDProxy for

global dataset was

• utset the model

dataset HDD.  Based on Inv-Density, 13 % of Inv-Density for global dataset was

• utside the model

dataset.

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Multidimensional Extrapolation Check : Leverages

 Leverage is calculated as the diagonal element (hii) of the matrix H. 𝐼 = 𝑌 𝑌′ 𝑌 −1 𝑌′ where X is the matrix of Predictor Variables  Interpretation  hii is “measure for distance of ith case in X from the mean of all the n values”  For the new values of Predictor Variables, leverage is 𝐼𝑜𝑓𝑥 = 𝑌𝑜𝑓𝑥

′

𝑌′ 𝑌 −1 𝑌𝑜𝑓𝑥  About 13 % of global data set may be under the effect of extrapolation using leverages.

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Scenarios Simulation at Global Scale

Projections of Future Energy and Emissions for Cities

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Application of Developed Tool for Global Estimation

 Global Energy and Emissions Estimation (2000)  Impact of Global Transition to Electric Vehicles.  Population and Density Change Impact.  Climate Change Projection Impact.

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Global Urban Energy Consumption (2000)

10 20 30 40 50 60 70

Electricity Heating Transportation

Urban Share in Global Energy and Related Emissions (2000)

Energy Emissions

% of Urban in Global

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8.35E+03 TWh 5.59E+10 GJ 4.54 E +10 GJ 5.32 GT-CO2 4.11 GT-CO2 3.14 GT-CO2

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Urban Energy (EJ)

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Urban Energy (EJ)

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Urban Energy (EJ)

Urban Energy (EJ)

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What if Urban Areas Switch to Electric Vehicles ?

 Carbon Intensity of grid is important for transition to Evs if reducing emissions is the goal.  Grid Intensity at which EVs become carbon competitive : 643 t CO2e/GWh (Hertwich et al, LCA of Evs)  𝐻𝐼𝐻𝐹𝑊 = 𝐻𝐼𝐻𝐽𝐷𝐹 ×

𝑗 643 where i = intensity of electricity grid.

 Scenario 1 : Hold the intensity i constant at year 2000. (Data from UNFCC)

 Emissions reduced from 3.14 Gt-CO2 to 2.98 Gt-CO2 (5 % reduction)

 Scenario 2 : Countries with i > 643 bring the intensity down to 643.

 Emissions reduced from 3.14 Gt-CO2 to 2.60 Gt-CO2 (17 % reduction)

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Scenario & Results

EVs as sustainable mode of transportation for emissions reduction only useful if countries reduce grid intensity.

Urban Density Change Impact

 High Projection : Density decline of 2 % per annum.  Medium Projection : Density decline of 1 % per annum.  Low Projection : Density decline of 0 % per annum.  Density Calculations 𝐸𝑢 = 𝐸𝑢−1 −

𝑞 100

× 𝐸𝑢−1 Where Dt = Density at tth year Dt-1 = Density at t-1th year p = Rate of density decline

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0.0E+00 5.0E+10 1.0E+11 1.5E+11 2.0E+11 2.5E+11 0% 1% 2% 0% 1% 2% 2020 2050

Electricity (MWH) Heating (GJ) Transportation (GJ)

34 % 104 % 80 % 72 % 89 % 165 % 72 % 213 % 308 % 72 % 420 %

Urban Density Change Impact on Energy

Urban Density Decline Urban Density Decline

33 % 34 % 36 % 54 % 34 % 67% 79 %

% Shown Over Year 2000 Consumption

Urban Density Change Impact on Emissions

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5 10 15 20 25 0% 1% 2% 0% 1% 2% 2020 2050

Electricity Heating Transportation

42.8 % 101 % 64 % 193 % 90 % 346 % 107 % 428 % 39 % 69.7 % 92 % 37 % 37 % 37 % 78 % 78 % 78 %

Gt-CO2 Reference Year for Emissions Increase : 2000 Ref Electricity Emissions : 5.32 Gt-CO2 Ref Heating Emissions : 4.11 GT-CO2 Ref Transportation Emissions : 3.14 GT-CO2 Urban Density Decline Urban Density Decline GHG Intensities held constant at year 2000.

218 %

Climate Change Impact on Urban Energy Consumption

 Climate change projections from National Center of Atmospheric Research (NCAR) based on IPCC scenarios and Community Climate System Model (CCSM3).  IPCC Scenarios

 IPCC-Commit : GHG concentration held constant at year 2000 and models run for 100 years of temperature projection.  B1 : Implements global solutions for economic, social and environmental sustainability issues.  A1B : Very rapid economic growth, growth of population till mid century followed by decline and introduction of more efficient technologies with balanced reliance

• n both fossil & non-fossil energy sources.

 A2 : Regional approach with heterogeneous economic and population growth.

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0.0E+00 5.0E+09 1.0E+10 1.5E+10 2.0E+10 2.5E+10 3.0E+10 3.5E+10 4.0E+10

0% 1% 2% 0% 1% 2% 2020 2050

IPCC-Commit SRES-B1 SRES-A1B SRES-A2

IPCC Scenarios : Impact on Electricity Consumption

MWh

Approx 32% Approx 53 % Approx 80 % 81% 76% 73% 75% 167% 163% 160% 162% 313% 308% 305% 307%

Urban Density Decline Urban Density Decline

IPCC Scenario Impact on Electricity Emissions

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2 4 6 8 10 12 14 0% 1% 2% 0% 1% 2% 2020 2050

IPCC-Commit SRES-B1 SRES-A1B SRES-A2

% Reduction in growth of Emissions over year 2000 SRES A1B Scenario wins for reducing electricity emissions over long term Urban Density Decline Urban Density Decline

IPCC Scenario Impact on Heating Emissions

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5 10 15 20 25 30 35 40 0% 1% 2% 0% 1% 2% 2020 2050

IPCC-Commit SRES-B1 SRES-A1B SRES-A2

% Reduction in growth of Emissions over year 2000 SRES A1B Scenario wins for reducing heating emissions over long term Urban Density Decline Urban Density Decline

Potential Model Developments for Renewable Energy Deployments

 Improved demand estimates using larger data set & region specific adjustments.  More variables such as GDP – move towards more regional models for urban areas.

 Resources accessibility can be a major driver for energy consumption

 Tavg for Urban area can be improved by testing the robustness of GIS methods for distance against monitored data.  Supply Side : Estimate rooftop solar potential for 3646 cities.

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Acknowledgements

 NSERC Accelerator Grant, Canada to Chris Kennedy, University of Toronto  University of Delaware, Center for Climatic Research, Department of Geography – For Climate Data. (http://climate.geog.udel.edu/~climate/)  Shlomo Angel and Collaborators for City Data. (http://www.lincolninst.edu/)  Iain Stewart : Urban Climatologist, University of Toronto

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Thank You

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