DOI: 10.3303/CET23107053 Paper Received: 03 June 2023; Revised: 25 June 2023; Accepted: 02 October 2023 Please cite this article as: Remsei S., Kolnhofer-Derecskei A., Reicher R.Z., 2023, Sustainability in Place Marketing, Chemical Engineering Transactions, 107, 313-318 DOI:10.3303/CET23107053 CHEMICAL ENGINEERING TRANSACTIONS VOL. 107, 2023 A publication of The Italian Association of Chemical Engineering Online at www.cetjournal.it Guest Editors: Petar S. Varbanov, Bohong Wang, Petro Kapustenko Copyright © 2023, AIDIC Servizi S.r.l. ISBN 979-12-81206-07-6; ISSN 2283-9216 Sustainability in Place Marketing Sándor Remseia,*, Anita Kolnhofer-Derecskeib,, Regina Zs. Reicherb aSzéchenyi István University Egyetem tér 1. Győr, Hungary bBudapest Business School, Markó u. 29-31., Budapest, Hungary remsei.sandor@sze.hu Changing consumer habits and social and economic changes have led most local authorities to rethink their marketing strategies to meet new challenges. However, marketing strategies often encourage over- consumption, so an ill-conceived marketing campaign can be fatal for a local authority. One of these is the cluster of literature dealing with the environment, environmental awareness, the impact of tourism, and the ecological footprint. The focus of this paper is to examine the cluster covering the topic of sustainable development. A prominent element of the SDGs is sustainable cities and communities, on which tourism can have a very significant impact, for instance, through transport emissions. The research examines the relevant indicators of the UN SDG 11 tracker (Our World in Data) and the OECD Better Life Index, systematically comparing the indicators covered by the study. The research results will show the inconsistencies, advantages, and disadvantages of the indicators in terms of sustainability and quality of life, which can help develop local marketing strategies. 1. Introduction In the previous phase of the authors’ research, a systematic literature review of Scopus, WoS, and Sage databases identified three well-defined clusters in place marketing, which formed a distinct entity within the field (Reicher et al., 2023). A systematic literature review on place marketing identified 6 clusters. One of them is the cluster of literature dealing with the environment, environmental awareness, the impact of tourism, and ecological footprint. Although the concept of sustainability was not identified as a separate cluster, the terms sustainability, sustainable, sustainability destination, etc., were found in all clusters when examining the number of occurrences of keywords. It leads to the conclusion that sustainability is also essential in place marketing. One of the concerns of the 2010 Stiglitz report (Stiglitz et al., 2010) is the economic measurability of the impact of housing on well-being. According to the report, "[t]he commonly used statistics may not be capturing some phenomena, which have an increasing impact on the well-being of citizens. For example, traffic jams may increase GDP as a result of the increased use of gasoline, but obviously not the quality of life" (Stiglitz et al., 2010), p. 307. Other researchers also dispute the suitability of economic indicators as a measure of well-being. Macroeconomic indicators do not give a complete picture of an individual's or a society's quality of life (Clark, 2018). The World Bank reports that strong economic growth in a country can also improve quality of life (QOL) over time, but this cannot be extended to the entire population (World Bank, 2020). GDP does not provide comprehensive information on the quality of the environment, the safety of housing, and the availability of leisure time for social participation and education (Guliyeva, 2022). Velazquez points out that quality of life is a more complex concept since it considers cultural, social, economic, and environmental dimensions (Velázquez, 2016). Affordable and sustainable transportation is also a key factor (Kwak et al., 2022). It can be assumed that good physical conditions do not necessarily lead to a liveable environment. Among the set of global targets, the SDG11 guidelines apply to municipalities. These targets are a universal call to action to end poverty, protect the planet, and ensure that by 2030, all people will enjoy peace and prosperity. 313 2. Relevance of the problem under study For urban development practitioners, the challenge is to measure, quantify, and create change metrics. In almost every field, the question is how to measure the impact of these changes and what is the best metric to describe the processes that have taken place and the future ahead. The conceptual framework within which the issue of sustainability is addressed and the thematic areas in which it is given prominence are crucial. One possible shortcoming of this topic is the divergence of the idea of a city- level evaluation methodology and the country-level data used, and as is known, within a country, there can be quite significant differences between individual cities, even within a city with characteristics of a metropolis. In order to avoid this ambiguity, we focused on the country-level indices and characteristics. 2.1 Databases examined The United Nations (UN) has adopted a set of global indicators (SDGs), refined and comprehensively reviewed annually. Indicators at regional and national levels complement the Global Indicator Framework. Among the SDG targets, the SDG11 guidelines apply to municipalities. According to the literature that draws on SDG11 data, the use of SDG indicators raises questions about what is being assessed by SDG indicators, and some researchers argue that the change needed for sustainability is not reflected in the current understanding of SDG indicators (Kopnina, 2016). Limited data availability and reliability issues make conducting thorough research difficult (Arfvidsson et al., 2016). Some publications suggest no new data sources or innovative ways of measuring SDG 11 targets (Satterthwaite, 2016). Critical comments on data and data reporting are valid, but the data officially provided for the SDG indicators can only be used. The analysis looks at the figures for the five priority areas and does not address the other SDGs. These targets are fully supported by the Organisation for Economic Co-operation and Development (OECD), whose database includes country data and indicators related to the SDG targets. Among the dimensions of well-being, the environment (current and future conditions) is given a prominent place, as reflected in the Better Life Index (BLI) maintained by the OECD (2023) as a possible indicator of well-being. The BLI is a composite index in which data from 38 member countries reflect altogether 11 themes that the OECD has identified as relevant to well-being in terms of material living conditions (housing, income, jobs) and quality of life (community, education, environment, governance, health, life satisfaction, safety, work-life balance). Each of the 11 themes in the index is currently based on one or four indicators. Within each piece, indicators are averaged with equal weights. The scores are primarily calculated based on data collected by the national statistical office of each country (using a standard unit of measurement, mostly with ratios). Table 1: OECD Indicators SDG11 equivalents OECD SDG11 Dwellings without essential services (%) Indicator 11.1.1 Proportion of urban population living in slums, informal settlements, or inadequate housing Rooms per capita Indicator 11.1 This measures the proportion of the urban population living in slum households. A slum household is a group of individuals living under a roof who lack one or more conditions: improved water supply, sanitation, sufficient living space, and housing durability Quality of support network (%) Indicator 11.3.2 Percentage of cities where civil society is directly involved in urban planning and management and which operate regularly and democratically. Air pollution (micrograms per m3) Indicator 11.6.1 is the annual average level of fine particulate matter (e.g., PM2.5 and PM10) in cities (weighted by population). Stakeholder involvement in the development of regulation (Average score) Related to 11.3.2. see as above Feel safe walking alone at night (%) Indicator 11.7.2 is the proportion of persons who have been victims of physical or sexual abuse by sex, age, disability status and location of occurrence in the last 12 months. In a detailed review of SDG11 (Sustainable Cities and Communities), for which ten sub-goals and 15 indicators have been identified, it has been found that measurement data are unavailable for 6 of the 15 indicators. Only 4 have some proportion of published data for the remaining nine indicators, allowing comparisons between countries. Still, even for these, there is little overlap between countries, which could allow for comparisons or clustering of countries on several criteria. Examining the OECD Better Life Index database, several indicators related to SDG11 targets among the other indicators have been found. The analysis was conducted only for 314 countries in the OECD database, and only the indicators associated with SDG11 in the OECD database were used. These are summarised in Table 1. On its website, the OECD allows visitors to declare where they live, so the site makes it visible how users rank welfare issues worldwide. The interactive map also shows which variables are the most important. Based on this, the OECD indicators that respondents emphasised have been selected, assuming that, for them, a positive score on this indicator represents satisfaction. It should be stressed that not all categories were included in the selected indicators, and not all indicators from one category were included (see Table 2). Table 2: Selected OECD indicators Category Indicator and unit of measurement Income Household net adjusted disposable income, USD Jobs Labour market uncertainty, % Education Education level, % Health Life expectancy, y Life satisfaction Life satisfaction - Average score Work-life balance Time spent on leisure and personal care, h 3. Methods The selected indicators were classified into two groups: the so-called hard indicators (upper part of Table 3) and the soft indicators (lower part of Table 3). The hard calculators are those that ensure the basic level of everyday life (living), mainly adapted from SDG 11. While the soft indices include labour-related well-being and quality- life values, those sourced from the OECD BLI. The latest available values (between 2015-2022) for 38 OECD and 3 non-OECD countries were considered. The average aggregated values for each country are listed in the last column. It is important to underline the importance of the sign and unit of measurement of each indicator (e.g. zero values for Dwellings without basic facilities or Air pollution mean that there is no such problem, while high values for other indicators such as Life satisfaction may indeed indicate greater levels of satisfaction). The average values are calculated by the OECD and reflect the arithmetic means of all data (i.e., all countries). This value provides a deeper insight into each value's meaning. Table 3: Table of merged indices Source: OECD database and SDG Tracker (Our World in Data team, 2023) Indicator Unit Average value Housing Dwellings without basic facilities % 3 Rooms per person - Ratio (1) 1.1 Community Quality of support network % 91 Environment Air pollution µg/m3 14 Civic engagement Stakeholder engagement for developing regulations Average score 2.1 Safety Feeling safe walking alone at night % 74 Work-Life Balance Time devoted to leisure and personal care h 15.07 Income Household net adjusted disposable income USD 3,490 Jobs Labour market insecurity % 5.1 Education Educational attainment % 79 Health Life expectancy y 81 Life Satisfaction Life satisfaction Average score 6.7 It is assumed that where physical conditions are ensured, they do not clearly lead to a more liveable, pleasant living environment. The physical environment is going to be detected through the hard indicators, while well- being and a pleasant environment are through the soft ones. The indicators selected from SDG11 and OECD BLI (see Table 3) are going to be compared. The sample countries may be clustered, and different clusters are assumed based on hard and soft indicators. Two groupings may be set up: one based on the sustainability aspect linked to the SDG11 target and the other based on indicators focusing on well-being. In sum, the two clusters can be merged where each sample country may be identified by sustainable characteristics. To construct the clusters, first, a so-called clustering technique was used with principal component analysis, and then the countries were clustered with K-means cluster analysis using SPSS. Since the values are adapted from statistical databases, assumptions are granted, and a parametric procedure was used for testing. 315 Here, observational methods were used with secondary empirical data sources from the above-mentioned indexes and theories, which were selected from statistical databases. Finally, different tests of independence were implemented. Consequently, the results of various countries can be observed by principal component analysis so that the countries may be clustered, and cluster analysis can be conducted. Both methods are so- called unsupervised, which means that they are based on simple statistical data; no external information about groups (or clusters) are used in order to obtain the clusters. The first one is a merging technique where group members are jointly based on their correlation (or closeness) towards observed indicators. Then, the countries are grouped through cluster analysis based on their similarities. 4. Results First, a descriptive statistical analysis of the variables selected above was carried out (see Table 3). It can be seen that the SDG11-based hard indicators differ from the soft indicators (both in meaning and values). In order to handle the different units of measurement, so-called standardised indicators (z-scores) were used. A principal component analysis was performed with the two sets of indicators for the countries included in the study. For the hard indicators, based on the correlations between the variables and the corresponding KMO value (KMO 0.730 and Bartlett's test p <.001), the explanatory power of the model is adequate (70.909 %), with a minimum of 2 large clusters. For the soft indicators, the KMO is lower (close to the threshold; KMO 0.578 and Bartlett's test p <.001), but here, 2-3 clusters can be assumed, with an explanatory power of 74.295 %. The first time, i.e., by hard factors, the Gower's similarity scores, which is more reliable than the Silhouette score in the case of mixed-type variables, were every time 0.5 or higher, proving a reasonably good clustering. In the second case, i.e., by soft factors, these values were higher over 0.6. Afterwards, so-called nearest neighbour hierarchical clusters were constructed, where dendrograms are shown in Figure 1. The dendrogram clearly reveals that the second cluster formed by the hard indicators has only one element, Mexico. Figure 1: Dendrograms of hierarchical clusters based on hard (left side) and soft indicators (right side) Source: The authors’ elaboration The hard indicators (left side) include more countries, while the soft indicators include fewer countries due to the lack of data. In the next step, 3-3 clusters were formed for both sets of indicators (using a K-means cluster analysis) that provided further investigations. The clusters were chosen to maximise the differences among cases in different clusters. In the first case, the observed significance level of “Stakeholder engagement for developing regulations” indicator did not show a significant impact, while “Time devoted to leisure and personal care” and “Life expectancy” from the soft indicators had the same weak impacts. 316 In the case of the hard indicators, countries can be classified into three clusters. The second classification also contains three clusters. Table 4 shows the average values of the clusters formed by the hard and soft indicators and the corresponding standard deviation values. For the clusters according to hard indicators, the third cluster performs best, with the most positive indicators. In this cluster, mainly Western European countries can be found. The situation is less clear for the soft indicator clusters. Here, the second and third clusters have similar positive scores. Spain and Greece belong to this cluster. Table 4: Cluster characteristics (mean and standard deviation) Source: The authors’ elaboration Hard Cluster Number of Case 1 (n=12) 2 (n=1) 3 (n=22) Total (n=35) Mean Std. Deviation Mean Mean Std. Deviation Mean Std. Deviation Dwellings without basic facilities 5.230 4.600 25.900 0.830 1.715 3.050 5.374 Rooms per person 1.290 0.261 1.100 1.900 0.264 1.670 0.399 Quality of support network 87.167 6.058 77.000 93.409 2.667 90.800 5.561 Air pollution 19.125 5.483 20.300 9.986 3.346 13.414 6.095 Stakeholder engagement in developing regulations 2.050 0.633 3.200 2.100 0.544 2.114 0.591 Feeling safe walking alone at night 64.667 12.879 42.000 80.682 7.587 74.086 13.378 Soft Cluster Number of Case 1 (n=12) 2 (n=2) 3 (n=6) Total (n=35) Mean Std. Deviation Mean Std. Deviation Mean Std. Deviation Mean Std. Deviation Time devoted to leisure and personal care 14.983 0.485 15.390 0.509 15.373 0.762 15.141 0.585 Labour market insecurity 2.925 0.898 18.750 4.172 4.800 2.114 5.070 5.019 Life expectancy 81.917 1.091 82.800 1.556 80.500 3.136 81.580 2.002 In sum, hard and soft indicators can be merged results in a merged classification (see Figure 1), where ten different combinations can be observed in the case of the studied countries. Table 5 shows a summary representation of both clustering methods and the merged clusters for some countries. Table 5. Cluster hierarchies in both indicators and merged clusters for some countries Source: The authors’ elaboration Country Hard Soft Group Austria 3 1 2 Sustainable and liveable countries Belgium 3 1 2 Canada 3 1 2 Finland 3 1 2 Germany 3 1 2 Ireland 3 1 2 Netherlands 3 1 2 Norway 3 1 2 United Kingdom 3 1 2 United States 3 1 2 Hungary 1 3 5 Less sustainable but moderately liveable countries Italy 1 3 5 Korea 1 3 5 Poland 1 3 5 Without being exhaustive, Table 5 only shows cluster pairs from the ten different variations where the pairings grouped more than three countries and data are known for both indicators. There are two larger clusters of countries, where one cluster contains countries rated as sustainable by the SDG11 targets and liveable by the OECD indicators, while in the other cluster, the SDG11 targets are not being met, but the soft indicators show that the countries are currently performing at more sustainable and moderately liveable levels. The analysis could find a group of countries where only hard or soft indicators were scored. There were also groupings where countries scored medium on both indicators. Finally, there were some countries that were not similar to any other countries, forming a cluster group of their own. These included Spain, Mexico and Greece. 317 5. Conclusions Researchers use numbers to describe the environment they are studying, so numbers, measurements and indicators are almost indispensable in research. However, it is essential to interpret these indicators well, explain them adequately, and understand the human being behind them. With the help of artificially produced indicators, it is possible to make different groupings and classify areas, social groups and characteristics. Some researchers assign chemical indicators, substantial numbers, to various measurable variables to describe the social, economic and natural environment. Other research focuses on softer indicators, where people's personal feelings play a more significant role in their perception of a region or country. Both paths are possible. This paper wanted to find out what similarities and differences there are if a specific area, urban development, is chosen and examined along both lines. The cluster analysis showed whether countries grouped by the hard indicators also belonged to the same group or not based on the soft indicators. The results show that there can be differences between countries classified as 'good' or 'fair' based on hard and soft indicators, as only a small number of the countries examined scored excellent on both indicators. This result highlights two points. One is that hard indicators should be treated and interpreted with caveats. But it also shows that people's perceptions might need to change because a country with positively rated soft indicators might not be sustained in the long term. Limitations of the research are that all results depend on statistical data reporting methods and methodology. Since SDGs aim to bear a comparable and uniform evaluation system, the creation of the metrics was intended to serve this single measurement option. Understandably, however, their interpretation can vary widely between countries and cultures. Agglomeration and infrastructure within a country are also important, as it is not possible to treat a whole country as a single area, e.g. within a country, there are also university towns, spa tourism centres, cultural sites, etc., so well-being is much more diverse. The second finding of the research is that complex (mainly environmental) indicators differ from welfare indicators, partly because factors other than environmental and economic (sustainability) elements may be necessary when choosing where to live, so a different scoring system may be used (see OECD BLI index). 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