How cities worldwide measure impact: International smart city indicators

The to-do list for a smart and sustainable city is long: more efficient street lighting, less energy waste, early warning of heavy rain or extreme heat, monitoring tree health, improving data accessibility and much more. The main goal for a smart city is to make life better for everyone.

But how do we know whether and how well a solution works? Are the paths taken the right ones? In the smart city this is answered by data-based indicators and impact measurement. In order to really capture and understand the effects of a complex system like city or municipality, a combination of indicator systems at intervention level and for the city as a whole is necessary.

Background: Brief theoretical introduction to indicators and impact measurement in the city

Gescribbeltes Schaubild, dass die Dimensionen von Wirkungsmessung in der Stadt zeigt. — Die unterschiedlichen Ebenen der Wirkungsmessung in der Stadt hängen letztendlich alle zusammen. Enoh Tabak

When it comes to indicators and impact measurement in cities, a distinction must be made between different dimensions, levels and scopes: At the level of single measures, it is necessary to understand the actual links between input and return. At a higher level, it is important to have a more precise overall picture of the city and its condition. Having both, you can analyse the effects of individual measures on the city or identify new measures and intervention gaps in a more targeted manner.

For defining a level of ambition, the smart city can both look internally at what resources and needs exist, and externally at how other cities do, for constructive comparison. The latter can be a great way to find orientation and motivation, but also to increase the attractivity of the location. It should always be recognised that measurements and descriptions can be both quantitative and qualitative, both subjective and objective. The diagram on the left shows how all these components are interrelated. The smart and sustainable city shall know and learn more about itself and thus be able to manage itself better.

Individual measures can be broken down into the building blocks of input, output, outcome and impact. Each resource used (input) achieves results and impacts with increasing scope and a broader frame of reference. The examplary measure of implementing an urban data platform illustrates this well.

The inputs here are various financial, human and structural resources, such as the payment of service providers, the working hours of the people involved in the project, the provision of data and technical interfaces for the platform, and so on. In the drawing above, these are the dark blue arrows, starting at the bottom left. The direct result (output) is the platform itself and the data that is managed by it. This is relatively easy and straightforward to assess, as the items are countable.

A broader result at a higher level (outcome) would be, for example, the (increasing) number of users of the data platform, the time saved in researching and retrieving data in specialised procedures or the higher number of data-based references in draft resolutions for the municipal council. Here, many indicators are measurable (rather than countable by hand), but may also need to be supplemented by qualitative queries. In addition, the change must be related to a previous value (e.g. "faster than...").

Within single projects, Objectives and Key Results (OKR) are a method of analytically linking the output and outcome levels. It is employed, for example, by the German smart city model projects in Freiburg and Cologne.

The outcome level can in turn influence a city's overarching objectives (impact level), such as climate neutrality or a balanced budget. However, as the (spatial and temporal) distance between the measure and the "object of measurement" is already greater at this impact level and there are additional, competing factors that influence it, determining causal relationships is much more difficult than with output and outcome.

Climate neutrality of a city, for example, is also influenced by greenery, the general economic situation and car park prices. It is difficult to isolate the single effect of the urban data platform. Methodologically, long-term studies and comparative analyses are often used for this level, so good preparation and clear and detailed information about the baseline scenario are crucial. It's the impact level that effects actial change in the overall dynamics of the city (green ellipse in the drawing) and clearly crosses the threshold of public perception.

The urban data platform, for that matter, can be both analyzed in terms of impacts on the city, but also as a prerequisite of doing so for any other measure, because it is the urban data platform that will usually map and combine data on indicators for any urban sector and the monitoring of the overarching goals.

The comprehensive view on the smart city - international indicator systems

Symbol des UN-Nachhaltigkeitsziels 11 „Nachhaltige Städte und Gemeinden“ – orangefarbenes Piktogramm mit stilisierten Gebäuden, einem Haus und einem Baum. — Das UN-Nachhaltigkeitsziel "SDG" 11 steht für lebenswerte, nachhaltige und inklusive Städte und Gemeinden. United Nations

SDG Monitoring

Urban development is assessed increasingly in a comprehensive manner in the international arena. The best known practice of that is probably the United Nations Sustainable Development Goals (SDGs). While initially formulated and standardised for the national state level, many local governments around the world subsequently decided to contribute to the SDGs within their own means and frameworks. Such efforts are summarised, for example, in the "City WORKS" toolkit of the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH.

In Germany, the municipal level of the Sustainable Development Goals is covered by the SDG portal. All municipalities with more than 5,000 inhabitants are listed here. Some cities also embed SDG monitoring in their municipal sustainability strategies. In addition to the 54 core indicators, which were formulated with the German Association of Cities and the German Association of Towns and Municipalities, municipalities occasionally add their own indicators or omit others.

The Berlin district of Treptow-Köpenick, for example, collects data on the number of cargo bike rentals as an indicator for Goal 11 ("Sustainable cities and communities"). Regensburg references several indicators and results of its digitalisation efforts to a number of SDGs, for example the ranking in Bitkom's Smart City Index for Goals 9 ("Industry, innovation and infrastructure"), 11 ("Sustainable cities and communities") and 16 ("Peace, justice and strong institutions"). This is often a good start, as SDG monitoring establishes clear links to an overarching, familiar narrative and connects with goals at national level. However, it primarily provides reference points for indicators at the impact level with respect to the overarching goals of a city.

And there is some aspects that warrant improvement or complementary efforts when it comes to the aspiration of smart and sustainable cities. For example, all data points in SDG monitoring are static and often date back several years. Many of the indicators are also based on surveys from higher administrative levels. Granular measurements directly from the city and with a spatial reference, such as those carried out by smart cities via sensors or crowdsourcing, are underrepresented. From the perspective of data-supported and integrated urban development, there is also a lack of broader operationalisation and more comprehensive indicators for the output and outcome level.

In addition, even local-level SDGs do not entirely cover some curical aspects of urban life, such as those relating to people's subjective well-being or (with the exception of broadband provision) those relating to digital infrastructure and transformation. This article therefore presents three other international examples of impact measurement in smart cities offering important additions.

Kreisförmiges Diagramm mit dem Titel „Valencia“, das die Leistung der Stadt in verschiedenen Bereichen wie Wirtschaft, Umwelt, Gesellschaft und Kultur darstellt. Die Visualisierung basiert auf dem U4SSC-KPI-Framework der ITU und zeigt Indikatoren zu Themen wie Energieverbrauch, Mobilität, Bildung, Gesundheit und digitaler Infrastruktur.ork der International Telecommunication Union (ITU). — City Performance Benchmark für Valencia nach dem U4SSC KPI Framework der International Telecommunication Union (ITU). International Telecommunication Union (ITU)

U4SSC Key Performance Indicators for People-Centred Cities

The "U4SSC Key Performance Indicators for People-Centred Cities" of the International Telecommunication Union (ITU) are based on the SDGs, but are more focussed on the use of digital technologies in the city. They provide for a more comprehensive set of indicators (91) and break down many of the overarching goals into standardised outputs and outcomes. Many of the typical smart city measures in recent years contribute directly to improving the KPIs mentioned here or enable their collection in the first place, for example through sensors.

Examples of indicators
A	Availability of public WiFi
B	Share of Smart Water Meters
C	Share of public transport stops with dynamic information on schedule
D	Number of open datasets
E	Access to Green areas
F	Reaction time of emergency services

A city participating in the U4SSC KPI framework receives, among other things, a concise overview of its status (see example from Valencia shown above). The U4SSC KPIs therefore offer a good public and internal reality check for the administration and are the fastest, standardised route to a smart city indicator system oriented towards the common good. The comparison of patterns with other international cities can also inform replication and learning potentials in the exchange with other cities.

Operationally, U4SSC offers a fixed and transparent support structure to collect the indicators in the city within a short time frame (3 to 6 months). The costs are staggered between USD 2,000 and a maximum of USD 30,000. Over 200 cities around the world have already used the U4SSC indicators. Cities from Germany and other countries are yet missing.

Liveability-Wellbeing City Indicators (LWCI) from Japan

Japan, often regarded as a pioneer in the development of smart cities, shows in many strategies already a more elevated impact-orientation, for example with clearly defined key performance indicators (KPIs) that make the success of individual interventions measurable. The smart city model project in Tsukuba, for example, formulated ex ante the target to reduce the proportion of private car use in the modal split by 2.3 % within three years or to increase the proportion of older people who consider their lives to be pleasant by 3 % (Prime Minister's Office of Japan, n.d.).

The Liveability-Wellbeing City Indicators (LWCI) further systematise the impact assessment at a city-wide level by combining both the (objective and subjective) quality of life and the (subjective) well-being of people in a total of 129 indicators. The LWCI is a combination of several indices, including the SDGs, but also (in their words) "specifically Japanese" indicators that focus on "interdependent wellbeing".

The following indicators are examples of the covered spectrum:

An objective indicator of quality of life in the mobility sector is, for example, the proportion of residents who can reach their local public transport system on foot.
Subjective indicators of mental quality of life are, for example, the weighted individual statements about whether many interesting people live in the neighbourhood or whether the city offers good opportunities for lifelong learning.
Indicators of "interdependent well-being" are, for example, self-assessments of the trust one places in a stranger or the proportion of older people living alone in the neighbourhood.

The collection of subjective indicators is relatively time-consuming and involves surveys. Hence the intervals between recorded data as well as their scope have limitations. To compensate for these weaknesses, there are also concepts such as "ActiveQoL" that want to collect more quantitative indicators of well-being in shorter intervals using wearables (see Suzuki, Tai & Sasao, 2025).

Based on this detailed and finely calibrated overview of the state of cities, methodological efforts are now being made in Japan to relate local policy measures to the indicator sets in a more structured way. For example, citizens were asked about their priorities for political measures. The individual options were then assigned to LWCI categories. Instead of wanting to improve all indicators at the same time, priorities were identified align well with the self-image of the city. The people of Kamakura, for example, focus on "coexistence with nature", "preparation against natural disasters" and the preservation of "art and culture" (Nagumo 2022).

A direct transfer of this comprehensive model would undoubtedly involve a great deal of effort. It is therefore helpful that core elements - above all the focus on well-being and the possibility of local contextualisation - are also being incorporated into a new framework at UN-Habitat.

Stadtansicht Vogelperspektive Tsukuba in Japan — Die japanische Stadt Tsukuba will den Anteil, den private PKW im gesamten Verkehrsaufkommen haben, reduzieren. Von On-chan - Eigenes Werk, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=20753317

QoL by UN-Habitat

UN-Habitat's Quality of Life (QoL) initiative is intended to make it easier for cities and municipalities to introduce an indicator system that is orientated towards quality of life and well-being. It has also taken inspiration from the Japanese works described above.

A distinction is made between a global layer with universal indicators and a local layer in which indicators are contextualized and formulated in participatory manner. Among the 47 predefined universal indicators are for example

subjective queries on the feeling of safety for pedestrians at night,
the objective affordability of housing or
the survey of participation in local cultural events.

International policy and project examples that have had positive effects on these indicators are referenced. For the local level, there are detailed guidelines for indicator development. To collect subjective indicators, it is recommended to feed in citizen-generated data from various sources, for example from surveys in social media, from existing tools (e.g. comments in city apps) or from data generated via crowdsourcing. An "impact platform" also provides four technologies to manage the indicator system:

A dashboard for dynamised representations,
a mapping tool,
an AI assistant for quick searchability and categorisation of the data and
a federated data system so that data is provided via interfaces but not "handed over".

So far, a preliminary version (Advance Release Version 1.0) has been published and there is a call for pilot cities in 2026 to test the framework.

Indicator systems for Smart Cities at a glance

SDG

U4SSC KPI

LWCI

QoL

Standardized broad and objective indicator set for impact level

➡️ SDG orientation should go beyond mere referencing of goals and colourful tiles and become streamlined.

Standardized objective indicator set with specific smart-city-indicators especially on output and outcome level

➡️ A quick baseline for internationally standardized and comparable smart city indicators.

Comprehensive, standardized objective and subjective indicator set combining quality of life and wellbeing in the city through several indices into a new impact level

➡️ Comprehensive indicators as inspiration for local contextualization.

Semi-standardized objective and subjective indicator set that differentiates between global and local layer and in sum converges on wellbeing for the impact level.

➡️ Facilitates a participatory process on impact measurement accentuating the wellbeing of inhabitants.

In conclusion: Three key points for better impact measurement in the smart city

Against the background of the abovementioned examples of international indicator sets and impact assessments in smart cities, three points are essential:

Better survey of the status quo.

For the SDGs, surveys already exist for all municipalities with more than 5,000 inhabitants in Germany. These should be consistently included in the conceptualisation of interventions and projects. A more detailed "smart city benchmarking" in accordance with the U4SSC KPIs can also provide clearer guidance for future projects and measures. It offers a constructive opportunity for comparison with other cities outside of the ranking logic and a relevant reference in future funding applications or in international cooperations. Active work with indicator systems is an important basis for achieving better impact measurement along all three impact levels (output-outcome-impact). The implementation of the U4SSC KPIs, for example, can be realised relatively quickly and cost-effectively.
Better target definition - where do we really want to go as a city?

A smart city learns more and more about itself and can express itself better and better. To define its goals properly, it should involve its people - also and especially in the definition of indicators and in full awareness of its objective and subjective or universal and local dimensions. The above-mentioned LWCI and the Quality of Life Initiative provide important inspiration for this. At present, it seems unlikely that the global community will be able to agree on even more specific goals once the SDGs for the UN's Agenda 2030 have expired. This makes it all the more important for local authorities themselves to be able to set ambitious targets and thus maintain their own future-readiness.
Measurement and impact assessment must be followed by analysis and action.

The abovementioned indicator systems are important foundations for a city-wide orientation to becoming a smart and sustainable city. Internal capacities need to be strenghtened to be able to carry out data-based impact analyses of local interventions. And it is particularly important to link measurements and surveys with concrete options and actions as early as possible and then to follow through with them - be it through political decisions, adjustments in service or in the future even with (partially) automated systems.

In sum: developing smart and sustainable cities without indicator systems is like driving without a speedometer. But without any steering or changes of gears, even the most precise dashboard cannot prevent accidents.

Contribution reflects the opinion of the author

Literature and References

Riedel, H.; Haubner, O.; Wolinda, M.; Stiftung, B.; Drees, S.; Städtetag, D.; Müller, A. (2020): SDG-Indikatoren für Kommunen. Indikatoren zur Abbildung der Sustainable Development Goals der Vereinten Nationen in deutschen Kommunen.

United for Smart and Sustainable Cities (U4SSC) https://www.itu.int/net/epub/TSB/2024-U4SSC-Key-Performance-Indicators-for-People-Centered-Cities-For-city-leaders/index.html#p=1 (Short summary for decision makers) [last access 12 november 2025].

Quality of Life Initiative – UN-Habitat.https://unhabitat.org/sites/default/files/2024/11/qoli-quality-of-life-initiative-implementation-guidelines-adv-release-v1.pdf [last access 12 november 2025].

Liveability and Well-Being City Indicators (LWCI) – Smart City Institute Japan. https://oascities.org/wp-content/uploads/2022/02/Tak-Nagumo-_CxC22_Monitoring-and-measuring-the-impact-of-digital-transformation-on-societal-priorities.pdf (Recording of a presentation during ISCN event) [last access 12 november 2025].

Nagumo (2022): Measuring and Monitoring Liveability and Well-Being for Smart Cities. Presentation at OASC.https://oascities.org/wp-content/uploads/2022/02/Tak-Nagumo-_CxC22_Monitoring-and-measuring-the-impact-of-digital-transformation-on-societal-priorities.pdf [last access 12 november 2025].

Prime Minister’s Office of Japan (o. J.): Japan Can Offer Various Solutions (Cases and Examples). https://www.kantei.go.jp/jp/singi/keikyou/pdf/Japan%27s_Smart_Cities-2(Cases_and_Examples).pdf [last access 12 november 2025].

Suzuki, K.; Tai, M.; Sasao, T. (2025): Smart City QoL-Based Assessment. Chapter 8 in Hitachi-UTokyo Laboratory (H-UTokyo Lab.), The Architecture of „Society 5.0“, pp. 113–125. https://doi.org/10.1007/978-981-96-2929-9_8 [last access 12 november 2025].