In Pursuit of High-Performance Global Cities –An Extended DEA Benchmark Model for Assessing Urban

Socio-economic Environmental Welfare Indicators

Soushi Suzukia, Karima Kourtitb,d and Peter Nijkampb,c,d

aHokkai-Gakuen University, Sapporo, Japanb KTH Royal Institute of Technology, Stockholm, Sweden

c Tinbergen Institute, Amsterdam, The NetherlandsdAdam Mickiewicz University, Poznan, Poland

Advanced Brainstorm Carrefour

Urban Empires -Cities as Global Rulers in the New Urban WorldAugustus 15, 2016, Adam Mickiewicz University, Poznan

Urban Empires –Cities as Global

Rulers in the New Urban World

Space in Transition• Peter Gould (1963): - Man against Nature - Locational patterns

• Lucassen and Willems (2011): - Challenge and Response - Adaptation and rising urbanisation

• Kourtit and Nijkamp (2012): - Globalisation and migration - Agglomeration advantages and creative cities

THE NEW URBAN WORLD

The New Urban World

2 Trends:• Persistent Urbanization• Fast Urbanization

Megatrends – The New Urban World• Rising urbanization everywhere (not every city)• Cities as ‘the home of man’• Urban areas as centres of development and of concerns• Pluriformity in urban appearance and socio-economic

development• Dominance of sustainability conditions (XXQ, Nijkamp,

2010)• No natural or economic limit to city size• The law of Van Loon (1932)• Smart specialisation• Need for effective long-range policy responses• Challenges for Regional Science

Figure 1. Percentage of population in city areas in Japan

• We live nowadays in the ‘urban century’.• The role of urban systems is becoming more and more important.

The megatrend of population concentration in city areas does not come to a standstill, even not in a depopulating and ageing society like Japan (Figure 1).

Urban Century

• Global cities play a role as global ‘rulers’ in the ‘New Urban World’.• In the globalization and environmentalization age, large

urban areas act as: - international communication stations, with a high

human intelligence ability and a powerful technological and socio-economic activity (Socioeconomic-cognitive activity),

- environmental coexistence stations, with a high-quality residential profile and an ecologically-friendly human environment structure

(Human environment profile).• There is a rising interest in ranking and rating systems

for cities on the basis of systematically designed comparative benchmark principles.

• A novel multidimensional analysis based on a Data Envelopment Analysis (DEA) which can evaluate an efficiency of Decision Making Units (DMUs) will be adopted in our study.

• Our study ties seeks to offer an advanced methodological contribution to the identification of high-performance cities (HPCs) on the basis of an extensive multivariate database on a set of 38 global cities.

• We also employ a ‘smart’ improvement strategy for less efficient cities in our sample, based on a newly developed efficiency improvement projection model in DEA.

Global cities in the GPCI database 

Source: Global Power City Index (GPCI) (2015), p.7

PerformanceImprovement

Projection

Performance Assessment

of Global Cities

Distance Friction Minimization (DFM) model

Preference-based (PB)

novel integration of all these elements

Methodological framework

Target-Oriented(TO)

PB-TO DFM model

Super-efficiency (SE) DEA model

Human environment

Socioeconomic-cognitive activity

Outline of DEA

uv,max

,0mv 0su

(FPo)

s.t.

: an efficiency score xmj : the volume of input m in DMU j

ysj : the volume of output s in DMU j

vm and us : the weights given to input m and output s

I1(x1)

I2(x2)

O

A C

BC’

( =OC’/OC)

DMU,1

mmjm

ssjs

xv

yu

mmom

ssos

xv

yu

DEA was developed to analyze the relative efficiency of Decision Making Unit (DMU), and projecting the performance of each DMU onto the efficient frontier.

• The efficiency improvement projection:

The original DEA models have only focused on a uniform input reduction in the improvement projections. The solution of an efficient improvement problem is not only just one point.

I1(x1)

I2(x2)

O

A C

BC’

( =OC’/OC)

DMU

• Suzuki and Nijkamp et al.(2010) proposed a DFM model that can compute more effectiveness solutions than the original projection.

AOriginalOriginal Projection

AADFM

DFM-Projection

Weighted Input 2(v2

*x2)

Weighted Input 1 (v1*x1)

• DFM does not need to incorporate subjective value judgments of a decision maker.

• Nevertheless, the strategies to improve a city’s performance are also based on political targets and preferences of city stakeholders.

• Therefore, in many decision-making situations, a balance between input and output targets has to be found. It seems more plausible that this balance is to be co-determined by a DMU’s preference pattern.

Outline of Distance Friction Minimization (DFM) Approach

• The target values in a Preference Based model, which are allocated between input efforts and output efforts based on Output Augmentation Parameter (OAP)(Examples OAP=0.7).

• This model is able to calculate both input reduction value and output increase value so as to reach an efficiency score of 1.0, despite the fact that in reality this might be difficult to achieve for low-efficiency DMUs.

Target Value(OAP=0.7)

Input score

Target value(DFM model)

70%

30%

(Input)(Output)

Fair allocation target

Output score

Preference Based(PB) Model in DFM

Input 1

Inpu

t 2

O

A

F

DC

B

E

F’

Normal DFM projection (TES0 = 1.000)

Non-Attainment DFM projection (θ*<TES0 <1.000)

CCR(original)-Projection

Target Oriented (TO) Model in DFM

takes for granted a given prior target-efficiency score (TES).

• This approach is able to calculate an efficient input reduction value and an efficient output increase value in order to attain this TES.

A Proposal for a PB-TO DFM Model

Distance Friction Minimization (DFM) model

Preference-Based Approach (PB)

Target-Oriented Approach(TO)

PB-TO-DFM model

Performance assessment of global cities No. DMU No. DMU No. DMU No. DMU1 Amsterdam 11 Fukuoka 21 Mumbai 31 Sydney2 Barcelona 12 Hong Kong 22 New York 32 Taipei3 Beijing 13 Istanbul 23 Osaka 33 Tokyo4 Berlin 14 Kuala Lumpur 24 Paris 34 Toronto5 Boston 15 London 25 San Francisco 35 Vancouver6 Brussels 16 Los Angeles 26 Sao Paulo 36 Vienna7 Cairo 17 Madrid 27 Seoul 37 Washington, D.C.8 Chicago 18 Mexico City 28 Shanghai 38 Zurich9 Copenhagen 19 Milan 29 Singapore 10 Frankfurt 20 Moscow 30 Stockholm

We refer to the “score by indicator” datasets in the GPCI-2015 report. These indicator data are converted into a standardized indicator value, falling in between 100 and 0, so that the data can be evaluated according to a uniform standard. The highest performance of an indicator receives a score equal to 100, and the poorest a score of 0.

Viewpoint 1: Human environment (human well-being, labour market and environment) We consider 1 Input and 4 Outputs :(I1) Total Employees(O1) CO2 Emissions(O2) Nominal GDP,(O3) Level of Satisfaction of Employees with their Lives,(O4) Percentage of Renewable Energy Used

38 Global Cities

Level of Satisfaction

Employees

CO2 Emissions

GDPRenewable Energy Used

Viewpoint 2: Socioeconomic-cognitive activity(human resources, communication, and cognitive performance)

We consider 3 Inputs and 2 Outputs :(I1)Interaction Opportunities between Researchers(I2)Research and Development (R&D) Expenditures(I3)Number of Employees(O1)Nominal GDP(O2)Number of Registered Intellectual Industrial Property Rights (Patents)

38 Global Cities

Employees

GDP Number of Patents

Interaction Opportunities

R&D Expenditures

Efficiency Evaluation Based on Super-Efficiency Model

No ‘double crown winner’ global city

we may need an efficiency improvement projection for inefficient cities.

Illustration of Efficiency Improvement Projection, Original Model vs DFM (Stockholm)

• CCR; reduction in Total employees by 23.1%, together with an increase in Satisfaction of Employees of 66.2% and a reduction in CO2 emission of 74.2%.

• DFM: reduction in Total employees by 13.0%, together with an increase in Nominal GDP of 18.5%.

• It appears that the empirical ratios of change in the DFM are smaller than in the CCR (more effective solution).

Illustration of Efficiency Improvement Projection, Original Model vs DFM (Amsterdam)

• These models are able to compute target input and output values to reach an efficiency score of 1.0; in reality this may hard to achieve.

• Reduction of R&D expenditures by 86.3% in a CCR model and by 80.8% in a DFM model… less feasible.

Efficiency-Improvement Projection of the PB-TO DFM model• The previous findings have demonstrated that the pathway to

an efficient outcome may require rather extreme ‘draconic’ measures and strategies.

• We will resort to PB-TO DFM model to explore whether an intermediate or mitigating strategy is possible in order to arrive at an entirely efficient city or to a pre-specified target level.

• Amsterdam - socioeconomic-cognitive activity;OAP is carried out in successive steps from 0.0 to 1.0 with intervals of 0.1, while the TES is set on 0.600 (note: the present efficiency score equals 0.426).

• Stockholm - human environment; OAP uses the same OAP, while the TES is set equal to 0.850 (note: the present efficiency score equals 0.769)

• OAP amounting to 0.7 (i.e., 70 percent of the total efficiency gap is allocated for output, and 30 percent of the total efficiency gap is allocated for input)

• a reduction in Number of Researchers of 5.9 percent, and an increase in Nominal GDP of 32.6 per cent are required to raise the efficiency score to 0.600.

Amsterdam: efficiency improvement of socioeconomic-cognitive activity

Stockholm: efficiency Improvement of Human Environment

• OAP is equal to 0.3 (i.e. 30 percent of the total efficiency gap is allocated for output, and 70 percent of the total efficiency gap is allocated for input)

• a reduction in Total employees of 6.3 percent, and an increase in Nominal GDP of 5.0 percent would be needed to raise the efficiency score to 0.850.

• If such a plan would have an OAP of 1.0 (i.e. 100 per cent of the total efficiency gap is allocated for output), then even an increase in Nominal GDP of 14.9% would be required to raise the efficiency score to 0.850.

Conclusions

• We have assessed 38high-performance global cities based on DEA.

• We also have presented a new methodology, the PB-TO DFM model. This model is able to provide operational and helpful step-by-step policy information on governance strategies of global cities.

• The results of this new methodology may provide a meaningful quantitative contribution to decision making and planning on the improvement of the performance for each global city, as illustrated by our case studies, and hence may reinforce the position of ‘urban empires’ in the ‘New Urban World’.