provides a good starting point for the process of learning and adaptation ..... The offered data and tools are intended to support the formulation and monitoring of.
Urban Benchmarking as a tool for complex assessment of development potential
Author: Jakub Rok, Centre for European Regional and Local Studies (EUROREG), University of Warsaw
Translation: Dorota Szmajda
Introduction Cities play a key role in ongoing development processes, and metropolitan areas are especially important as hubs of global innovation networks which offer various socio-economic challenges and opportunities. The necessity to consider concurrently a wide range of phenomena associated with smart, inclusive and sustainable growth – identified as main challenges in the Europe 2020 Strategy – raises the need for comprehensive tools for diagnosing the development potential of cities. One of the most appreciated methods is Urban Benchmarking, allowing for a comparative analysis against a flexible set of indicators. The essence of urban benchmarking is the comparison of indicators describing a given territorial unit, e.g., a city or metropolitan area, with similar indicators describing other units. As a result, there is a clear diagnosis of the level of development of a unit as compared to a selected reference group. Urban benchmarking allows identifying the main opportunities and challenges of a given area, particularly in relation to the adopted strategic priorities - hence its usefulness for local authorities in conducting evidence-based policy. Both the process of selecting indicators and dissemination of the results of diagnosis also provide an opportunity to strengthen the mechanisms of social participation. Urban benchmarking is particularly effective as a method for a relative evaluation of results in measuring complex phenomena for which no unequivocal measure of success can be found.The obtained results are more than just ‘meaningless’ numbers as they provide information on the relation of these phenomena to other similar units.Thiscomparison-based methodreflects the natural human wish to evaluate one’s position vis-à-vis the position occupied by other persons in the close surroundings. An effective diagnosis of the status quo (position, results obtained, etc.) provides a good starting point for the process of learning and adaptation to the existing actual conditions. In addition, comparing one’s performance with that of othershas always been a popular way to find new, more effective solutions. We can identify five major objectives of urban benchmarking (prepared by the author, based on Cowper, Samuels 1997*):
To objectively assess the performance of the city or specific spheres of its activity(e.g. quality of selected public services)
Cowper, J. & Samuels, M. (1997) Performance benchmarking in the public sector: The United Kingdom Experience, Paper presented at the OECD Meeting on Public Sector Benchmarking. oecd.org/unitedkingdom/1902895.pdf *
Toidentify areas where improvement is needed
To find comparable units or entities with a superior performance with a view to using good practices, i.e. transfer and adaptation to the conditions of a given city
To evaluate the effectiveness of programmes intended to restructure and improve the operation of a given city
To enhance accountability to various groups of stakeholders, particularly the public at large.
Stages of UB analysis 1. Defining the framework conditions of the analysis a) Identification of the aim and type of study (1) What is to be the object of benchmarking – processes or results? (2)
What
is
benchmarking
intended
in
relation
to
–
other
unitsor
specified/externalstandards? (3) How is benchmarking to be used – as a tool supporting continuous improvement or as a tool for assessment? b) Overview of strategic documents (European, national, regional, local) for the purpose of identifying current goals and development challenges c) Determining the scope of analysis, e.g., a list of subject areas included in the comparison d) Determining selection criteria and the choice of reference units 2. Data compilation, calculation and visualisation of results e) Overview of available databases (e.g., national statistical offices, Eurostat, ESPON Database) for the necessary indicators f) Verification of data completeness, their time range, construction and the comparability of particular variables/indicators g) Selection of indicators h) Calculations i) Visualisation - graphic presentation of the results (diagrams, maps) 3. Interpretation and discussion j) Verification of outlying results (surprisingly good or bad, taking extreme values) k) Assessment of relative strengths and weaknesses of the analysed unit, i.e. positioning relative to the reference group and objectives
l) Analysis of trends over time (if data depict different time periods) m) Informing the wider public about the results (other clerks, citizens, media) n) Determining development goals for the city based on the results of the diagnosis Comparison with similar methods (SWOT analysis, nexus model) SWOT analysis is a popular analytical technique categorising available information about a
given
phenomenon
into
four
groups
of
strategic
factors,i.e.
strengths
and
weaknesses,opportunities and threats, while the Nexus model is a tool used to identify development opportunities and challenges, taking into account the specific geographical features of a given area.The concept of the model was developed and applied in the ESPON GEOSPECS project, drawing partially upon the lessons learnt during theearlier ESPON TEDI project. Urban benchmarking
SWOT analysis
Origin
New Public Management; initially in the private sector, focus on competitiveness and measuring the quality of services in administration
Strategic management; initially in the private sector
Essence
Structured comparison
Objective
1) Objective assessment of city’s performance 2) Identification of areas for improvement 3) Transferof good practices, learning from leaders 4) Assessment of the effectiveness ofrestructuringprogrammes 5) Strengthening of accountabilityand civic participation
Qualitative strategic analysis Basic objective is to identify internal and external factors significant for achieving a given goal. This helps better analyse competitive advantages and choose suitable operational strategies.
Practices
Bottom-upapproach, involving policy makers and other stakeholder groups at all the evaluation stages. Three types of benchmarking: 1) process benchmarking: analysis of process(es) focused on specific result in a group of organisations; emphasis on understanding differences and identification of good
Two basic interpretation components, i.e. (1) matching – finding competitive advantages by combining strengths with opportunities, and (2) transforming – converting weaknesses and threats into strengths and opportunities. SWOT analysis is frequently combined with TOWS analysis that helps
Nexus model Medical and environmental sciences; focus on contextualisationof analyses and endogenouspotential of regions Contextualised strategic analysis 1) Identification of areas with strong development potential 2) Combining development processes with specific regional geographical features 3) Offering an alternative for convergence-based approach 4) Incorporating soft processes into strategic analysis Division of analysed regions intocategories based on specific features of geographic environment(e.g. mountains), with a view to identifyinga network of interdependent processes shaping growth in such areas. Factors determining the region’s environmental features are the starting
practices; 2) results benchmarking: comparing results of similar organisations in selected areas; emphasis on improving efficiency. 3) standards benchmarking: measuring results of organisation in respect of agreed standards; emphasis on monitoring of results and continuous improvement Many visualisation tools such as graphs, maps, visualisations, m.in. graphs, maps, infographics 1) willingness of policy Success makers to choose objectives factors based on analysis’ results 2) willingness to learn from others and be compared with others, while avoiding a ranking-based approach 3) including process measures to better analyse how interventions are planned and implemented Source: prepared by the author
outline four basic strategic paths: expansion (maximaxi), embracing opportunities (mini-maxi), overcoming threats (maximini) and striving for survival (mini-mini).
point; they are combined with opportunities and threats posed by so-called intervening processes Visualisation of results, typically in the form of a diagram
Visualisation of results, typically in the form of a matrix. .
1) setting achievable goals 2) relevant prioritisation of factors
1) correct identification of linkages between the geographic features, threats and opportunities that can be influenced by public intervention2) balanced contextualisation and possibility to make comparisons between regions
A review of ESPON 2013 projects in terms of BMapplication ESPON 2013 projects can provide valuable support in conducting urbanbenchmarking. This includes methodological supportassociated with the structure of the entire evaluation process (BEST METROPOLISES, GEOSPECS), technical support associated with reviewing major goals and development challenges faced by cities (SGPTD, FOCI),and support in data selection (European Urban Benchmarking Webtool, ESPON Database). ESPON FOCI http://www.espon.eu/main/Menu_Projects/Menu_AppliedResearch/foci.html The project was implemented in the years 2008-2010 by aconsortium of partnersled by theFree University of Brussels.The project entitledCities and urbanagglomerations:their functionality and development opportunitiesfor European competitiveness and cohesion(ESPON FOCI) was intended to diagnose the development potentials of European cities, evaluate the role of functional linkages for increasing their competitiveness, social cohesion and permanence of development processes. Emphasis was placed on the largest metropolitan centres in Europe, regarded as growth poles. With a view to devising scenarios for future
development paths of European cities, analyses of their economic, transport, scientific and regional linkages and ties were conducted. One of the significant results of the study was the identification of weak linkages existing between Central and Eastern European metropolises and their regional hinterlands. For instance, the environs of Warsaw are losing more as a result of backwash processes than they do as a result of spread processes. The report summarising the project’s finding can provide important insights for the evaluation of key challenges and opportunities relating to the development of European cities, particularly urban policy making and strategy formulation; for example, comparison of the rates of economic growth in the metropolis and its surroundings may be useful both in diagnosing challenges and trends and in selecting entities/units for the reference group. Typology of macroregions based on GDP growth, 1995-2004
Source: ESPON FOCI Scientific Report, p. 260
ESPON Second Tier Cities and Territorial Development in Europe http://www.espon.eu/main/Menu_Projects/Menu_AppliedResearch/SGPTD.html The ESPON project entitled Second Tier Cities and Territorial Development in Europe: Performance, Policies and Prospects(SGPTD) aimed to define the role that non-capital cities play in economic development and offer an answer to the question on how investments in such cities help increase prosperity nationally. The project was implementedinthe years 2010-2012 by aconsortium of partners led by theEuropean Institute for Urban Affairs. The study comprised 184 major non-capitalcities. In addition, detailed case studies were conducted for 10selected cities. One of the key findings from the SGPTD project is that non-capital cities manifesta relative flexibility and remarkable resilience to economic crises. The study also found that investments in such cities brought higher GDP rate increases than was the case in the capital cities. TheSGPTDproject demonstrated how basic indicators of economic growth can be used tomake interesting comparative analyses. It also serves as a valuable complement to the FOCI project as it offers an analysis of development challenges facing medium-sized cities.
BEST METROPOLISES http://www.espon.eu/main/Menu_Projects/Menu_TargetedAnalyses/bestmetropolises. html The projectBest Development Conditions in European Metropolises: Paris, Berlin and Warsawwas a study of metropolitan development using the examples of Paris, Berlin and Warsaw. It predominantly set out to investigate how metropolisation processes impact the development potential of the central cities and their metropolitan areas. The analyses focused on three thematic areas: mobility,living standards and metropolitan management. The project report can be regarded as a model example of a practical application of the BM method, with its clear and succinct presentation of the differences between the development levels of the three cities in selected thematic areas.The analyses were accompanied by detailed descriptions of the social and economic profiles of the metropolises concerned. The findings revealed considerable backwardness of Warsaw in nearly all of the analysed areas, particularly with regard to the efficiency of transport, the situation of which is aggravated by a constantly increasing share of private transportand the overloading of the city’s onlyundergroundline. The report can be treated as a summative source of indicators to measure the development of large urban agglomerations; it identifies main areas of metropolitan development, methods for its measurementand presentation of results.
Benchmarking for the metropolitan areas of Paris, Berlin and Warsaw
Source: ESPON BEST METROPOLISES, Draft Final Report, p. 16.
GEOSPECS http://www.espon.eu/main/Menu_Projects/Menu_AppliedResearch/geospecs.html The projectGeographic Specificities and Development Potentials in Europeaimed to offer a diagnosis of the development potential of regions with specific geographic environment features, and examined mountainous, transborder, insular, peripheral, sparsely populated and coastal areas. Thestudy was implemented in the years 2010-2012 by a consortiumled by the University of Geneva. The goal of the project was to develop a methodology for evaluating the development potential of regions with specific geographical features, identify their potentials and differences within specific types of areas. The researchers questioned theusefulnessof benchmarking in evaluating the results achieved by such regions, and pointed to the need to address their specific features more strongly. The nexus model was proposed instead, with its emphasis on the identification of areas with a strong development potential, based on combining development processes with the region’s specific geographical features. The methodology devised as part of the project can be used to further enhance the urban benchmarking method, particularly with regard to regions with some territorial specificities. This would involve for example making an evaluation as part of a reference group selected on the basis of a criterion consistent with thecharacteristic feature of its geographical environment (e.g. in coastal regions) or making an attempt to isolate phenomena derived from
the specific features of a given territory and devise a customised strategic analysis of threats and opportunities. Nexus model for sparsely populated areas
Source: ESPON GEOSPECS Final Report, p. 36
CityBench http://espon.geodan.nl/citybench/# The project ESPON CityBench for benchmarking European Urban Zonesis intended to develop an Internet platform offering user-friendly presentation of the data compiled as part of ESPON. CityBench is being developed by the Geodan Holding based in the Netherlands. It is expected to provide research tools for experts, policy makers, public officials, private and public investors, help them identify potential opportunities and threats to development and make comparative analyses of cities using various indicators and methodologies,particularlythe benchmarking method. The project is to be completed by the end of February 2014. Currently, the first round of city selection has been finalised†, while the conceptual work on the web application is still under way. Ultimately, a ready-to-use tool will be created, with a database offering information about some 700 European cities.This information will be divided into six thematic areas: accessibility, economy, natural environment, knowledge, quality of life, demography.The indicators will be derived not only from traditional databasessuch asESPON, Eurostat, but also †173 larger urban zones (LUZ) were selected, including 12 LUZs located in Poland: Białystok, Bydgoszcz, Gdańsk, Kalisz, Katowice, Kielce, Kraków, Lublin, Łódź, Poznań, Szczecin, Warsaw.
from innovative sources such as networking media and those based on the data from users of spatial information systems (volunteered geographic information). CityBench is expected to provide a reliable source of indicators forurban benchmarkingboth nationally and internationally. The Internet application will allow independent benchmarking of selected cities and support various forms of visualising results. User interface of the planned CityBench home page
Source: ESPON CityBench Interim Report, p. 38
ESPON Database database.espon.eu/db2 http://www.espon.eu/main/Menu_Projects/Menu_ScientificPlatform/citybench.html ESPON Database is an Internet website intended to give access to all indicators which are calculated as part of various ESPON 2013projects. The database has been designed to suit the needs of both researchers andpractitionersin the sphere of urban and regional management. The offered data and tools are intended to support the formulation and monitoring of territorial development strategies. Currently, the website offers access to several hundred indicators, of which some refer to the level of cities (with data coming mainly from the ESPON FOCI and Urban Audit projects). Documents with methodological guidelines and those supporting data compilation can also prove particularly useful. Logged-in users can also have
access to applications for creating maps on several different scales (e.g. global, European or local) and various data processing tools. The website is regularly updated, mostly with new sets of data uploaded upon project completion. One of the ideas contemplated for the future is the publication of case studies on the website. BM databases and on-line tools useful in UB analysis
Eurostat epp.eurostat.ec.europa.eu/portal/page/portal/region_cities/regional_statistics epp.eurostat.ec.europa.eu/portal/page/portal/region_cities/metropolitan_regions
OECD Regional Statistics and Indicators www.oecd.org/gov/regional-policy/regionalstatisticsandindicators.htm
Urban Audit http://www.urbanaudit.org/compare.aspx
European Cities Monitor http://www.europeancitiesmonitor.eu/
Globalization and World Cities Research Network http://www.lboro.ac.uk/gawc/
Urban Ecosystem Europe informed-cities.iclei-europe.org/map/
The Reference Framework for Sustainable Cities (RFSC) www.rfsc-community.eu/resources/rfsc-step-by-step/
Practical examples of UB use Example 1 - regions The starting point for this exercise is an attempt to compare the performance of socioeconomic regions, whose economies had until recently relied on heavy industry and coal mining, and which were then forced to restructure their economic base. The examples ofsuch areas in Poland include Upper Silesia (Górny Śląsk) and Dąbrowa Basin (Zagłębie) and we will focus on these two at the later stages of the analysis. First, we should answer three basic questions, viz.:
What are we benchmarking? focus on the results of changes
What is the benchmarking relative to? similar regions from other EU countries will form the reference group
How is benchmarking to be used? the aim of the exercise is to make a relativised evaluation of the economic situation
Familiarisation with the context of strategic activities should come as the next step in the process. Since in this case we are interested mainly in the European perspective, it is useful to invoke the goals inscribed in the Europe 2020Strategy. Its three priorities include smart, sustainable and inclusive growth. Of the main performance indicators of this strategy, labour market indicators are of particular interest to us, i.e. employment rate for the 20-64 age brackets and the indicator illustrating the threat of social exclusion. These measures provide the starting point for defining the scope of the analysis. Thedemographics as well as basic economic indicators such as GDP and labour productivity figures can also provide interesting information in view of the future of post-transformation regions. The next step is the selection of the reference groups. Our study is based on the approach that compares regions with a similar history of economic transformation (coal-based development, major role of heavy andmining industries) and includes the European dimension. Since we decided to conduct a benchmarking of results and not the process as such, it is not necessaryto identify and include the leader into the group. Instead, we will focus our activity on seeking out similar regions. First, we intend to narrow the search area to Central and Eastern Europesince the restructuring of the coal mining sector inEngland, France or Spainprogressed in a completely different way than was the case in Poland. Second,we plan to focus our attention on regions with a comparable size, particularly in a relative approach, i.e. in relation to their respective countries. This means that we are interested in those regions which have a significant impact nationally, and therefore will attract the interest of the state authorities. In this way, we get a list of several regions, i.e.the Ruhr, Saarland (Germany), Ostrava Basin (Czech Republic), Jiu Valley (Romania). The next step will be to specify the list of indicators,together with the identification of their availability in selected databases. Our study will be conducted using ESPON HyperAtlas, a sophisticated tool for examining the performance of selected entities or units relative to various reference groups, which ensures reliability and full comparability of the indicators for the whole of Europe and supports visualisation of results. When we know the contents of the HyperAtlas dataset, we will be able to convert the categories proposed earlier for comparisons into specific indicators, viz.:
Labour market indicators (in line with the Europe 2020Strategy, we would like these figures to showthe level of employment in the 20-64 age bracketsand of social exclusion). A review of the database helps match two measures, i.e. economic activity rate of peopleaged 15-64 and unemployment rate.
Demography, an important factor for theregion’s future. The selected indicator is the percentage of young people(15-29 age brackets) in the overall number of the economically active population.
The region’s economic strength, based on which we can assess whether the specific locations still act as economic drivers or not. The indicators are selected from the database: GDP per capita based on PPP‡, labour productivity based on PPP.
Before starting the calculations, we need to address one more issue associated with the nature of the data made available in the HyperAtlas dataset. The provided indicators can be accessed from the NUTS-2 level (regions). This means that the selected locations need to be ‘translated’ into the NUTS-2 category. This can be problematic in two situations:the Ruhr region is divided into twoNUTS-2 areas (Arnsberg and Düsseldorf), whilst the Jiu Valley represents a relatively small part of the extensive Vest region in Romania. In the former case, we decided to include the Arnsberg region into the analysis due to the location of Dortmund, the Ruhr region’s unofficial capital. As regards the Jiu Valley, the consideration mentioned above should be taken into account during further analyses. Unemployment rate (2005) is the first indicator to be analysedas it reflects social exclusion in terms of labour market indicators. The figure below is a print screen from HyperAtlas, showing a fragment of a map illustrating unemployment level in NUTS-2 regions in relation to Central and Eastern Europe. For convenience, names were provided for the regions under analysis.
‡ Purchasing power parity is used in comparisons between countries as it takes into account the differences in the costs of living in specific countries.
Unemployment rate (2005), relative to Central and Eastern Europe
Source: prepared by the author based on ESPON HyperAtlas.
An analysis of the map shows that unemployment is a serious problem in this group of regions as compared to the overall level in Central and Eastern Europe. This is particularly well visible in Germany and theCzech Republic, where the unemployment level in the remaining regions of the former FRG is as a rule lower than the average forCentral and Eastern Europe (12%). The Silesian (Śląskie) region with unemployment rate at 19%, scored the worst result among the analysed entities. Nevertheless, Moravia-Silesia with 175% unemployment rate ranked worst when benchmarked with the national average. The relatively good place of the Romanian region can be explained by the issue of the scale mentioned above;the Jiu Valley Basin has a relatively little impact on the Vest region. Economic activity rate is another analysed indicator. The map below shows the performance of specific regions relative to the respective national average. In case ofPoland, Czech Republic and Romania, the ‘coal’ regions are characterised by a relatively high rate of economic activity at a level of 71-72%, which represents 101-103% of the average for a given country. It should also be noted that this indicator rather poorly shows the differences between regions, and therefore analysing the actual figures will add little value to the evaluation of the performance of the entities in question.
Economic activity rate (2005), relative to the national average
Source: prepared by the author based on ESPON HyperAtlas.
We survey the demographic prospects of regions based on the share of young people in the economically active population. The values of this measure are shown in the figure below. Blue, i.e. the recurring colour for nearly each of the analysed regions, indicates a low percentage of the young in the labour force compared to the neighbouring regions. Silesia scores well in this regard, with 98% of the average for the neighbouring areasand 100% of the averagefor Central and Eastern Europe. Share of people aged 15-29 in the total economically active population (2005), relative to the average in the neighbouring regions
Source: prepared by the author based on ESPON HyperAtlas.
The strength of the region’s economy is the final selected category. This time, the map represents a concise approach to the visualisation of results of the GDP per capita indicator, i.e. the typology of regions based on the three deviation categories discussed earlier – from the average for Central and Eastern Europe (so-called large deviation), from the national
average(so-called medium deviation) and from the average for the neighbouring areas (socalled large deviation). The legend in the top left-hand corner provides information about the nature of the specific types of regions. Silesia has been includedinto the group of regions where GDP per capita is higher at all the three levels of comparison – European, national and relative to the neighbouringunits. Alsothe analysed regionsin Germany and the Czech Republic have a higher GDP than the Central and Eastern European average, but lower than their respective national average. In case of the Czech Republic, this may be connected with inflating the average values by the separate Prague region. The Romanian region is below the Central and Eastern European average, but reaches high values with regard to lower spatial scales. To sum up, this can be viewed as proof of the continued economic strength of these regions (particularly in case of the new Member States). GDP per capita (2005), typologyof regions
Source: prepared by the author based on ESPON HyperAtlas.
Labour productivity is the last indicator to be analysed. The map below shows the results of this measure relative to the national average.Only two regions – Silesia in Poland and Vest in Romania - reach values over 100%, but the nominal productivity figures place these two regions at the bottom of the ranking. It can be expected that this situation is due to a relatively large share of people employed in agriculture in the two respective countries. Low labour productivity in this sector significantly lowers the country’s average,thus rewardingindustrial regionswith a relatively high urbanisation rate.
Labour productivity (2005),relative to the national average
Source: prepared by the author based on ESPON HyperAtlas.
To ensure a better transparency of the results, the table below summarises all the resultsachieved by individual regions. Two values were provided for each of them, i.e. nominal value and value relativeto the national average (in italics). The units with the best results in a given categoryare shown in green, and those with the worst – in red (in nominal values). This helps better evaluate the strengths and weaknesses of individual regions, particularly if the observable trend for the nominal values wholly overlaps with the deviation from the national average. Benchmarking results
Region
Unemploym ent rate
Economic activity
% of young people in economically active population
GDP per capita [PPP]
Labour productivity [PPP]
Silesia (PL)
19% | 107%
72% | 103%
33% | 96%
12400 | 108%
17200 | 105%
Moravia-Silesia (CZ)
14% | 175%
72% | 101%
31% | 100%
14400 | 85%
20000 | 83%
Vest (RU)
7% | 93%
71% | 101%
33% | 95%
8870 | 113%
12500 | 111%
Saarland (DE)
11% | 97%
66% | 99%
25% | 95%
25400 | 97%
38600 | 98%
Arnsberg (DE)
12% | 109%
66% | 98%
26% | 99%
24300 | 93%
37100 | 95%
Source: prepared by the author based on ESPON HyperAtlas
These findings suggest an answer to the question posed above, concerning various aspects of the performance of industrial regions undergoing difficult restricting processes. Firstly, these regions must still grapple with high unemployment associated with the collapse of traditional industrial sectors. As a result, the authorities need to pursue an effective policy focused on retraining and finding uses for the indigenous infrastructure and skills in some attractive niches (such as for example change of the business profile by some shipyards which
started to manufacture marine wind turbines). High unemployment is also induced by a relatively high rate of economic activity, particularly in the new Member States. This is a valuable potential ofthese regions, offering an opportunity to meet the employment objectives of the Europe 2020 Strategy. At the same time, the share of young people in the total number of the economically active population is relatively low. In this regard, Silesia can be viewed favourably, which means that the region has demographic potential with which development processes can be stimulated. Therefore it is particularly important to facilitate young people’s entry onto the labourmarket and offer them attractive career paths. Although Silesia’s economic strength still ranks the region above the national average, as the share of employment in agriculture dwindles and the service sector expands, this dominance willprobably shrink (cf. the exampleof the Ostrava Basin). This indicates the need to support research and development activitiesand diversify the region’s economic structure in order to increase the presence of high added value sectors. Example 2 - cities This section offers a comparison of cities from the same region, the Kujawsko-Pomorskie region in Poland, in one selected area – environmental protection. First we will set the framework for the analysis, by answering three basic questions:
What do we want to benchmark? We are mostly interested in results.
What do we want to benchmark the selected units with? We will analyse cities with the district rights in the Kujawsko-Pomorskie region, and the evaluation will be formulated in respect of the average value for the cities in this group.
How is the benchmarking to be used? As an evaluation technique, since developing a tool for continuous improvement would require much more thorough analyses.
As mentioned above, we are interested in the evaluation of results achieved by these cities in the sphere of environmental protection. Therefore, we will make references to the relevant strategic documents to ensure a solid basis for the selection of the study areas and application of the indicators used. The environmental goals formulated in the regional strategies are alsoimportant as they will help decide which challenges are particularly pertinent in a given region. The review of the strategic provisions with relevance for the specific cities at national and regional levelsidentified seven key challenges in the sphere of environmental protection:
Consolidation of spatial management
Increasing the role of renewable energy
Improving energy efficiency, ‘green’ building
Improved air quality
Access to clean water
Rational waste management
Popularisation of pro-environmental behaviours
An analysis of this list outlines six key areas that should be measured. These are: spatial management, energy, air quality, water quality, waste managementand environmental awareness. The group of surveyed entities was identified during the early stage of research. In terms of the regional development strategy, the absence of Inowrocław on the proposed list could be seen as a problem. However, its inclusion would considerably restrict the pool of available data, since - unlike the four remaining cities – the indicatorvalues for Inowrocławwould need to be derived from the NUTS-5 level, that is – municipalities. Another step is to ascribe specific indicators to the thematic areas of the survey. A review of the Polish statistical data led to the formulation of the following indicators:
Spatial management: share of the area comprised by local zoning plans to the total area (1.local zoning plans)and share ofgreen areas in the total urban space (2. green areas)
Energy: [lack of data at NUTS-4 level]
Air quality: emission of gas and air particulate pollution from especially noxious plants per capita (3. emission)
Water quality: municipal and industrial sewage treated biologically, chemically and with increased biogenic removal in treatable sewage (4. waste)
Waste management: mixed waste collected from households per capita, inkg (5. waste)
Environmental awareness: pipeline water consumptionin households per 1 user (6. water consumption)
Outlays indicator:budget expenditure in cities with district rights on air and climate protection, sewage and waste management, water protection, per capita (7. environmental expenditure).
We decided to add one more outlays (expenditure) indicator to complement the result indicators. Although on its own it will not be sufficient to evaluate the effectiveness of the
performance of a given local government, it will certainly expand the perspective in which the actual results can be examined. In order to enhance data credibility, we calculated the values for most of the factors for the functional urban area, i.e. the city and its surrounding municipalities (with the exception of the indicators showing the share of green areas). As a result, we were able to minimise the impact of arbitrarily delineated administrative areas while taking into account the effects of the ‘urban sprawl’ processes onto the adjacent areas situated in the neighbouring municipalities. For the outlays indicator, we used the average for the threeyear period in order to reduce the random effects of large one-off investments. In an attempt to enhance the analytical potential of the exercise, we decided to use a dynamic approach, comprising both the situation at present (2012), and that in the past (in this case – 2005). This will allow observing trends concerning changes in the results achieved and in the relative position of the surveyed cities. In order to increase the transparency of results, we present them in two different ways, i.e. asnominal and relative values that is,in the latter case, the percentage of the average for the four entities under analysis. Benchmarking results – evaluation relative to the average
2,5
1. spatial planning
2
2. green areas [%] 1,5
3. emission 6. sewage
1 5. waste [kg] 0,5
6. water consumption 7. expenditures
0 Bydgoszcz
Torun
Grudziadz
Wloclawek
Source: prepared by the author based on the Local Data Bank
The diagram above helps assess the relative position of the cities in a given sphere. For instance, we can say that the share of the areas with local zoning plans is similar in Bydgoszcz, Toruńand Grudziądz and perceptibly lower in Włocławek. We can also assume the perspective of a single city and describe the results of e.g. Włocławek. We can say therefore that, in
comparison to the remaining cities included in the comparison, the city is characterised by huge air pollutant emissionand large environmental protection expenditure (with values nearly twice above the average). The areas in which Włocławek performs superblyinclude water treatment (120% of the average), relatively low volume of waste per capita and low water consumption. Its problem areas include air pollution, low share of green areas and a relatively weak position in terms of the local zoning plan coverage. If we are more interested in looking at the nominal values of the analysed cities, we should benchmark the results to an ‘external’standard such as the national average or thresholds proposed by experts or defined in the course of social consultations. Another step is to incorporate the dynamic approach into the analysis. The table below shows information about the current results and how they changed in the period 2005-2012. The colours illustrate the city’s relative rank in a given category in 2012; the more intense the colour the further the result is from the average, and green shows the desirable direction of this deviation. Information about the changes in the result achieved is presented in numerical form, as a percentage of the 2005 result. Scores over 100 indicate that a given phenomenon has increasedfurther, and below 100 – that the relevant values fell. It should be noted that the indicators vary in character – in case of green areas and level of sewage treatment, an increase will be viewed positively, and negatively in all the other cases. We also added information about changes in environmental protection outlays to complete the picture. Benchmarking results: status quo and changein 2005-2012
2.green areas
3. pollution
4. sewage
5. waste
6. water 7. consumption environmental expenditure
Bydgoszcz
104
73
95
89
90
108
Toruń
115
43
46
122
96
47
Grudziądz
108
102
104
114
95
104
Włocławek
100
96
105
143
95
538
Source: prepared by the authorbased on the Local Data Bank
The analysis will be more thorough if we set the information about the status quo against the observable trends concerning changes. For instance, we can identify problem areas, e.g. the low rank of Toruń in the category of sewage treatment, due to a definite deterioration of the situation in the recent years. Similarly, the relatively good score of Toruń in the sphere of air quality can be associated with a dramatic drop in pollutant emissionin 2005-2012. Another methodwould be to start with the present weaknesses and look at the change trends –e.g.
Włocławek and Toruń have a relatively low share of green areas, but only the latter city recently made attempts to redress this situation. We can also examine the dominant trends in a given area and look for anydeviations which could provide the grounds for an in-depth analysis. For instance, Bydgoszcz was the only cityto reduce the volume of household waste. It would be worth finding out what the underlying factorswere and whether these solutions could be used elsewhere. Finally, we could compare the results with the values showing expenditures. For example, it is worth finding out what the relevant funds were spent on in Włocławek: the relevant amount increased fivefold, but their potential effect in the analysed spheres can hardly be noticed. To sum up, the findings presented above invite some conclusions concerning environmental protection in the cities with district rights in the Kujawsko-Pomorskie region. The most pertinent environmental challenges include the insufficient (and still deteriorating) level of sewage treatmentin Toruńand a high level of air pollution in Włocławek. Another major challenge is spatial management in the functional area of the latter city. An increasing volume of waste generated in three out of four surveyed cities is also quite alarming – a thorough analysis of the arrangements adopted in Bydgoszcz could offer an opportunity to overcome this negative trend. Water consumption has slightly fallen, although it is not clear whether this should be attributed to the environmental awarenessof the city residents or rather to the increased fees. Bydgoszcz seems to be the regional leader in the sphere of environmental protection, which is the more interesting because no substantialincrease of budget spending has been observed in this area, and the total expenditure is much lower than in the neighbouring Toruń. Moreover, Grudziądz – a city that also performs relatively well in the area in question – has definitely the lowest expenditures on environmental protection, keeping them at an unchanged level for the past seven years.
ESPON projects and tools ESPON 1.1.1, Urban areas as nodes in a polycentric development ESPON BEST METROPOLISES, Best Development Conditions in European Metropolises: Paris, Berlin and Warsaw ESPON CityBench for benchmarking European Urban Zones ESPON 2013 Database, espon.eu/main/Menu_ToolsandMaps/ESPON2013Database ESPON FOCI, Future Orientation for Cities ESPON GEOSPECS, Geographic Specificities and Development Potentials in Europe ESPON HyperAtlas, espon.eu/main/Menu_ToolsandMaps/ESPONHyperAtlas ESPON SGPTD, Secondary Growth Poles and Territorial Development in Europe; Performance, Policies and Prospects ESPON TEDI, Territorial Diversity in Europe
