SUTI pilot application in four cities

Transcription

1 SUTI pilot application in four cities - preliminary results Henrik Gudmundsson, Consultant to UN ESCAP Chief Advisor, CONCITO Capacity Building Workshop on Sustainable Urban Transport Index Colombo, Sri Lanka October 2017

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3 Cities / Urban regions Kathmandu Valley 2.8 mill. Inh. 722 km2 3,878 inh./km2 Jakarta JABODETABEK 30.1 mill. Inh. 6,767 km2 4,448 inh./km2 Hanoi City 7,7 mill. Inh. 3,359 km2 2,292 inh./km2 Colombo Western Region 5.8 mill. inh. 3,684 km2 1,774 inh./km2 Images: Henrik Gudmundsson

4 The SUTI pilot process Cities committed following Regional ESCAP Meeting in Jakarta, March 2017 Data collection guidance ready mid-august 2017 All four focal points have completed data collection and submitted report before workshop October 2017 All four focal points have delivered all 10 SUTI indicators and index for each city Hard work. Creative solutions. Already drawing implications for planning. Excellent!! Some issues and adjustments remaining

5 All of the following results are tentative, preliminary assessments as part of SUTI pilot testing May not reflect actual performance or ranking of cities

6 SUTI score per city *) (preliminary) Jakarta Kathmandu Colombo Hanoi *) Geometric mean

7 Counting scores Indicator Jakarta Hanoi Kathmandu Colombo 1 1 3,5 3, ,5 3, No No 4 1 4,5 1 3,5

8 Jakarta Hanoi Kathmandu Colombo 10. Greenhouse gas emissions from transport Av Air quality (pm10) 1. Extent to which transport plans cover public transport, intermodal facilities and 100,00 90,00 80,00 70,00 60,00 50,00 40,00 30,00 20,00 10,00 0,00 2. Modal share of active and public transport in commuting 3. Convenient access to public transport service 8. Investment in public transportation systems 4. Public transport quality and reliability Av Operational costs of the public transport system General higher performance Av Affordability travel costs as part of income Av Traffic fatalities per inhabitants Av General lower performance

9 Question 1) To what extent are grouped scores, Result of genuine good or poor performance? Result of the scaling factor (min and max posts), which may skew the result? Mostly genuine Mostly scaling Mixed/unsure 5. Fatalities 6. Affordability 10.GHG Emissions 4. Quality and reliability 7. Operational costs

10 Jakarta Hanoi Kathmandu Colombo 10. Greenhouse gas emissions from transport 9. Air quality (pm10) 1. Extent to which transport plans cover public transport, intermodal facilities and 100,00 90,00 80,00 70,00 60,00 50,00 40,00 30,00 20,00 10,00 0,00 2. Modal share of active and public transport in commuting 3. Convenient access to public transport service 8. Investment in public transportation systems 4. Public transport quality and reliability 7. Operational costs of the public transport system 5. Traffic fatalities per inhabitants 6. Affordability travel costs as part of income Low variation in performance Medium variation in performance High variation in performance

11 Variations Very similar: 10 GHG s Vastly different: 2 modal split 4 PT quality 8 investments In between : 1 plans 3 access to PT 5 fatalities 6 affordability 7 operational costs 9 air quality Likely to be different reasons for differences

12 Question 2) To what extent are differences among cities, Result of genuine difference of performance? Result of differences in data and methodology in deriving values for each indicator? Both play a role depending on the availability of data and use of methodology in each city for each indicator

13 Data quality (crude assessment) Data quality (completeness, validity, recency) Indicator Colombo Hanoi Jakarta Kathmandu Average Average Limited 2. Fair 3. Good

14 Transparency (crude assessment) Transparancy Indicator Colombo Hanoi Jakarta Kathmandu Average Average Limited 2. Fair 3. Good

15 Comparability (crude assessment) Comparability Indicator Colombo Hanoi Jakarta Kathmandu Average Average Limited 2. Fair 3. Good

16 Jakarta some strong/weak points Indicator Stronger points Caveats 1. Plans The most comprehensive plans for active and PT New indicator, method for scoring may differ across cities 5. Fatalities Lowest fatality number Cities are quite close 8. Investment in Public Transport By far the highest share (max) Indicator Weaker points Caveats Very limited comparabiliy across cites for this indicator 2. Modal share Quite high motorization Maybe data issues (taxi) 6. Affordability High costs for transport Data issues (exclude cost for private, compare with low income group) 7. Operational costs for PT Other observations Quite low cost recovery rate = high subsidy Fair comparability Remarkable with low pollution GHG emissions considering high motorization

17 Hanoi some strong/weak points Indicator Stronger points Caveats 4. Quality and reliability of PT By far the highest score, May improve even more? 6. Affordability Very affordable for low income groups 10. GHG emissions Lowest emission/capita Indicator Weaker points Caveats Good data, but comparability for other cites are low Good data, but comparability of other cites are low Cities are very close Best data work of all cities 2. Modal share Extreme high motorization? Maybe different for only commuting 7. Operational costs for PT 8. Investment in Public Transport Low cost recovery rate (= large subsidies) Extremely low share of investments in PT Fair comparability Low comparability across cities Low value is hard to understand 9. Air Quality Worst pollution Due to methodology (PM2.5) Other observations Strong variation in performance Hanoi report has the most transparent documentation

18 Kathmandu some strong/weak points Indicator Stronger points Caveats 2. Modal share Second best performance Partly the result of low income? 3. Accessibility to Public Transport 7. Operational costs for PT By far the highest score Positive cost recovery Indicator Weaker points Caveats 1. Plans Plans are weak especially for cycling 4. Quality and reliability of PT Other observations Very low score (below scale) Low data quality, Low comparability; need better data Low data quality, Low comparability; need better data Low data quality, Low comparability; need better data Kathmandu seems to have the most data quality challenge A bit surprising that Kathmandu SUTI is second among the four cities Kathmandu has less extreme performance (no 4 ); may help explain some of this

19 Colombo some strong/weak points Indicator Stronger points Caveats 1. Plans Second best scoring plans New indicator Maybe implementation cahellenges 2. Modal share Best share for active and public transport Data seem good quality and transparent 9.Air Quality Has the lowest pollution level Methodology issues; Less dense; vicinity to ocean? Indicator Weaker points Caveats 3. Accessibility to Public Transport 4. Quality and reliability of PT Lowest score Extreme low score 5. Fatalities Lower performance than other cities Other observations Data seem credible due to excellent methodology Significant methodology and comparability issues Hardly because of including rail accidents Limited data availability at urban/regional level; using national level data Best share for active and Public Transport, while having the highest GHG emissions

20 Summary for indicators (1) 1. Plans Seems to work very well despite being a new indicator Maybe apply peer review to enhance comparability? 2. Modal share Data collection (travel surveys) is a major challenge/cost Challenge to measure only travel to work and school 3. Accessibility to Public Transport 4. Quality and reliability of Public Transport Important indicator for SDG target 11.2 Methodological challenge but some cities manage Low comparability across cities Extreme variation in results Methodologies to measure user satisfaction are in principle well defined, and a city like Hanoi manages well However data and methods differ, limiting comparability Need to rescale min and max to Asian cities? 5. Fatalities The most transparent and comparable indicator Only one city (Colombo) collected fatality data for nonroad fatalities should this be dropped?

21 Summary for indicators (2) 6. Affordability A challenging indicator for some cities Problems to apply same definitions data across countries especially for income of low income group 7. Operational costs for Public Transport 8. Investment in Public Transport Some data challenges to obtain cost recovery especially for private companies Using crude proxy values likely influence reults Extreme variation in performance Least comparable indicator Significant challenge to identify data for urban/local PT investments What is appropriate time horizon, 3-5, or more years? 9. Air Quality Limited availability of annual mean PM10 data in the cities and therefore limited comparability 10. GHG emissions Very good and similar performance for cities indicate a need to rescale to Asian context

22 Final note Issues of limited data quality, transparency or comparability for the pilot study at this stage does not necessarily mean that these problems are general for all cities, or could not be solved with a bit more time or extended dialogue Still, there are are likely some deeper challenges for regular data collection and comparison for some of the indicators

23 Thank you!