Monitoring and modelling the urban component of the carbon cycle

Transcription

1 Monitoring and modelling the urban component of the carbon cycle Michael Raupach ESSP Global Carbon Project CSIRO Marine and Atmospheric Research Centre for Australian Weather and Climate Research and Peter Rayner Thanks: GCP colleagues, CSIRO colleagues Realizing Low Carbon Cities: Bridging Science and Policy, February 29, Nagoya, Japan

2 Introduction and Outline Introduction Urbanisation is one of the great present trends in the earth system: interacts with population, human aspiration, connectedness, ecological limits Urbanisation involves changes to modes of production and consumption Need to understand and influence - urban patterns urban processes urban management Outline Background: global and regional trends in emissions, population, GDP, energy The fine spatial picture as seenm from space

3 Global trends in urbanisation UN Development Program data (August 26) 8 World 15 More Developed 5 Populatio on (million) 7 Urban > 1M Urban 5M to 1M Urban 1M to 5M Urban.5M to 1M Urban <.5M Rural Less Developed Least Developed

4 Raupach et al. (27) PNAS Updated to 25 with IEA data Drivers of global emissions F Kaya Identity G E = P P G F E 1.5 FEtotal Population 1.4 g=gp/p e=e/gp 1.3 f=f/e h=f/gp Fossil-fuel CO 2 emission Population Per-capita GDP Energy intensity of GDP Carbon intensity of energy World Carbon intensity of GDP = F/G = (E/G) x (F/E)

5 Raupach (28) unpublished Development trajectories: energy Plot per capita primary energy against income, from 198 to 25 energy (k kw/person) E/P Per capit ta primary Per capita energy, E/P [kw/person]? Taiwan in 25 = China in 23? Taiwan in 198 = China in USA EU Japan D1 FSU China Idi India D2 D3 World Australia France Taiw an KyotoA1 Income (g=gp/p)

6 Raupach (28) unpublished Development trajectories: CO 2 emissions Plot per capita FF emissions against income, from 198 to 26 Per cap ita FF emis F/ ssions /P (tc/ /y/person) Per capita emissions, F/P [tc/y/person] USA EU Japan D1 FSU China India D2 D3 World Australia France Taiw an KyotoA1 Income (g=gp/p)

7 Urbanisation: global spatial data Population density (GRUMP) min max Nightlights (DSMP-OLS) Raupach, Rayner, Paget (29) (Submitted to Energy Policy)

8 Raupach, Rayner, Paget (29) (Submitted to Energy Policy) Nightlights (L) versus population density (D)

9 Probability distributions, ranks, power laws Exceedance probability distribution of random variable is the probability of exceeding a given value (x) Relationship to rank: if set of observed x is ranked in descending order, EPD(x) = (rank of x) / (number of observations) Power-law hypothesis: EPD(x) ~ x p 1 Zipf plot: Populations of largest urban regions and cities (25) Urban Regions Cities slope 1 slope 2 A "scale-free" distribution 1 "Zipf's law" of city sizes Popula ation (M) Rank

10 Exceedance probability distribution (EPD) for nightlights and population density Raupach, Rayner, Paget (29) (Submitted to Energy Policy) Log(EPD) Nightlights Log(L) Log(EPD) Nightlights Log(L) Slope 1.8 Slope 1.8 USA Europe Japan D1 FSU World China India D2 D3 World Log(EPD) Population Density Log(D) Log(EPD) Population Density Log(D) Slope 1.8 Slope 1.8

11 Raupach, Rayner, Paget (29) (Submitted to Energy Policy) Physical-economic indicators and nightlights Energy (kw/km 2 ) Emissions (tc/y/km 2 ) r 2 =97.97 r 2 =96.96 NLcorr NLcorr GDP-PPP (k$/y/km 2 ) USA Europe Japan D1 FSU China India D2 D3 World r 2 =.94 NLcorr

12 Rayner, Raupach, Paget, Ciais (In prep) CO 2 emissions map from data assimilation FFDAS = Fossil Fuel Data Assimilation System Assimilates nightlights, population density, national physical-economic data Accounts for nightlights saturation and other data errors Yields map of areal density of emissions (tc/y/km 2 ) In future, can also assimilate other data sources including CO 2 from space min max

13 Summary Nightlights data are a powerful resource, AFTER accounting for saturation Power-law Exceedance Probability Distribution (EPD) is observed for nightlights g and population density Power-law EPD allows estimate of nighlight lost to instrumental saturation: World 3.4% USA 16% Japan 54% Saturation-corrected nightlights, with population density, can provide spatial densities of energy consumption, emissions, GDP Fossil-fuel Data Assimilation System (FFDAS) Yields emissions maps from nightlights, g population, p economy Later can assimilate CO 2 from space Output is a high-resolution emissions map which is consistent with local and national information aggregate emissions received by the global atmosphere

14 Hilary Talbot

15 Abstract and extra slides Urbanisation represents not only the tendency of a population to live in cities and towns, but also the adoption of associated modes of production and consumption. The latter factor is the reason why the global trend towards urbanisation has major implications for the carbon cycle, climate and the earth system. To explore these implications, three big questions need to be addressed: What are the space-time patterns of urbanisation and its effects? What are the governing processes? What are the opportunities for creative management? This talk will briefly address the research agenda implied in each of these questions. Patterns: The discernment of patterns requires better information, which needs to be both global and sufficiently finely resolved to see cities and towns. I will indicate the information available from combined use of nightlights (from satellite observations), population-density data and regional data on physical and financial economies. Processes: Urbanisation is one visible reflection of four great trends which are together determining the evolution of the earth system in the present anthropocene era: population and its dynamics in both developed and developing regions, human aspiration and its consequences, globalisation and interconnectedness of economies and cultures, and global ecological l limits it on natural resource use, including climate and the carbon cycle. Improved understanding of the syndrome of processes linked with urbanisation can elucidate much about how these great trends operate and interact. Management: To reduce consumption, and its impacts on climate and ecosystems, new urban forms are needed. The management challenge is both to imagine these new forms and to devise the pathways for evolving our cities towards them.

16 Feedbacks in the carbon-climate-human climate system 1 8 Emissions GHG sinks R1 R2 R3 Land, ocean systems R1 R2 R GHG emissions C1 C2 C3 Human societies and economies C1 C2 C3 Atmospheric GHG concentrations Global climate system [CO2].4 Regional.2 climates R1 R2 R Temperature

17 Components of radiative forcing CO 2 other gases nongas IPCC (27) WG1

18 Global temperature predictions for 19 to 21 (IPCC Fourth Assessment, Feb 27) Future CO 2 emissions (2-21) High A2 17 GtC Medium A1B 15 GtC Low B1 92 GtC None ~25 GtC Actual CO 2 emissions 2 deg Climate system inertia IPCC (27) Fourth Assessment, WG1 SPM, Fig 7

19 A phase transition in human ecology 1 Global per capita GDP GWP per capita (Y2 $US / person / y) Since 18,,global wealth and per-person resource use have doubled every 45 years Growth in consumption: essential before 19 disaster after Global population and GDP 1 doubling time = 45 y AD Population (million) GWP (billion Y2 $US / y) Population GDPppp 1 Angus Maddison ( 1 AD

20 Global CO 2 emissions from fossil fuels to 27 Emissions from fossil fuels and industry (CDIAC data) Year Emissions (GtC/y) Growth rates (CDIAC data) Decade Growth rate % y % y % y 1 Graphs: Raupach et al. (27) PNAS, with updated data: CDIAC to 27, IEA to 25 Fossil Fue el Emission (G GtC/y) Foss sil Fuel Emiss sion (GtC/y) CDIAC IEAall A1B(Av) A1FI(Av) A1T(Av) A2(Av) B1(Av) B2(Av) CDIAC IEAall A1B(Av) A1FI(Av) A1T(Av) A2(Av) B1(Av) B2(Av)

21 USA FEtotal Population 1.8 g=gp/p e=e/gp 1.6 f=f/e h=f/gp EU Japan D FSU China India D D

22 Regional Shift in Emissions Share Emissions from developing countries are growing fast Percent age of Glo obal Annua al Emission ns 62% 38% FCCC Kyoto Reference Year 57% 43% Kyoto Protocol adopted 49.7% 5.3% Kyoto Protocol enters into force 53% 47% Current J. Gregg and G. Marland, 28, personal communication

23 August 26 Populations of largest urban regions and cities 1 largest urban regions 1: Tokyo (35.2M) 2: Mexico City (19.4M) 1: Casablanca (3.1M) Total: 693M (1.7% of global) Popu ulation (M) largest cities 1: Mumbai (12.78M) 8 9: Mexico City (8.5M) 6: Pune (3.6M) 6 Total: 351M (5.4% of global) 4 Tokyo region (35M) Populations of largest urban regions and cities (25) Urban Regions Cities 2 We are talking about urban settlements of all sizes! Rank

24 Urban and rural incomes: China Per capita income of urban and rural households in China, Heilig, G.K. (1999) Can China feed itself? A system for evaluation of policy options. IIASA. ( Caption: This chart partly explains the attraction of cities and towns for China's rural population. Whereas average household income has risen significantly in rural areas, incomes in urban areas have increased even more. The gap between urban and rural income has remained almost unchanged. Source: China Statistical Yearbook, Beijing, 1998 (p.325) Note: Constant prices.

25 Power-law exponents and missing nightlights fraction Power-law exponents: 1.2 to 2.8 (smaller in brighter regions) Fraction of nightlights unseen because of saturation: 3.4% (World) 54% (Japan) Region Exponent p Mean L (counts) Nightlights (L) Population density Exponent p Mean D (person km 2 ) Missing nightlights fraction because of saturation, m L USA Europe Japan D FSU China India D D World