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Sustainable development is one of the most urgent challenges facing humanity. Its basic principle is to improve people’s well-being and maintain it over time. In 2015, the United Nations approved 17 sustainable development goals (SDGs) to ensure a better and sustainable future for all, balancing economic, social and environmental development. SDGs create an ‘indivisible whole’; thus, examining their interactions is crucial. Our goals were twofold: (i) to assess the implementation degree of SDGs in EU countries and (ii) to examine the interactions between goals. The potential to achieve a given SDG is approximated by a composite indicator, calculated based on an innovative method of implementing tools derived from sensitivity analysis (SA). Respecting the degree of variability of individual variables and their correlation, we set the weights to equalise their importance. Moreover, the application of SA allows us to remove strongly correlated variables that are not transferring supplementary information. We use countries’ linear ordering and Spearman’s rank correlation coefficient to assess interactions between SDGs.Our research shows that Scandinavian countries predominately occupy the leading positions, respectively, eight times on the podium for Sweden and four by Denmark (of which three as a leader). The Netherlands also stands out, occupying the superior position in the performance of SDGs 2, 3, 5, 8, 9, 12 and 17. The top in achieving one of the most priority area – no poverty (SDG1) belongs to Czechia. Our study confirmed the results obtained by many researchers regarding the more potent synergy between SDGs compared to the trade-offs. The only trade-offs observed in our study relate to SDG15, which negatively correlated with SDG3 and 17. Furthermore, SDG7 was the only one not associated with others.

The multifunctional Ecosystem Service supply analysis at the spatial level is often the output of a weighted sum of layers in a Geographic Information System (GIS). This procedure is weak in detecting and representing the relationships between the input layers. Nonetheless, composite indicators produced by overlaying techniques are quite common in applied research and their discrepancies are underestimated in the scientific community, thus affecting the quality of resulting composite maps. In this work, we empirically test the effectiveness of multivariate statistics to obtain reliable composite Ecosystem Maps in the Turin metropolitan area (north-west Italy). We apply the Principal Component Analysis (PCA, using Matlab and ESRI ArcGis) to seven Ecosystem Service models (Habitat Quality, Carbon Sequestration, Water Yield, Nutrient Retention, Sediment Retention, Crop Production and Crop Pollination) and we evaluate how much the resulting composite map differs from the traditional GIS overlay. In doing this, the spectral analysis (with eigenvectors and eigenvalues) of the covariance matrix of the normalized layers confirms the heuristic arguments about the dependence between Ecosystem Services. We show that the PCA method can provide valuable results in landscape Green Network design, avoiding the limits of standard overlaying procedures. Finally, smoothing and classification techniques, applied to PCA estimates, can further improve the approach and encourage its use in various ecological indicators.

Composite indicators are very popular tools for assessing and ranking countries and institutions in terms of environmental performance, sustainability, and other complex concepts that are not directly measurable. Because of the stakes that come with the media attention of these tools, a word of caution is warranted. One common misconception relates to the effect of the weights assigned to indicators during the aggregation process. This work presents a novel series of tools that allow developers and users of composite indicators to explore effects of these weights. First, the importance of each indicator to the composite is measured by the nonlinear Pearson correlation ratio, estimated by Bayesian Gaussian processes. Second, the effect of each indicator is isolated from that of other indicators using regression analysis, and examined in detail. Finally, an optimisation procedure is proposed which allows weights to be fitted to agree with pre-specified values of importance. These three tools together give developers considerable insight into the effects of weights and suggest possibilities for refining and simplifying the aggregation. The added value of these tools are shown on three case studies: the Resource Governance Index, the Good Country Index, and the Water Retention Index. [ABSTRACT FROM AUTHOR]

The aim of this paper is to propose a class of composite indicators for measuring well-being at the local level, which takes into account the variability between and within the local units. Although we believe that well-being is a multidimensional concept and cannot be reduced to a single measure, we also stress the importance of aggregating the information of several well-being indicators into a reduced number of composite indicators, one for each well-being domain, which play a crucial role in policymaking and benchmarking. As an application, we focus on the Equitable and Sustainable Well-being of the Italian Provinces. In particular, based on a dataset containing 41 elementary indicators, we aggregate them by domain, comparing different aggregative approaches and illustrating the difference in the rankings of the Italian Provinces they produce. Finally, as an illustrative example, we focus on a member of the class of composite indicators that accounts both for vertical variability (between the Provinces) and horizontal variability (within the Provinces). The construction of a composite index for each domain allows us to evaluate and compare multidimensional well-being among the Italian Provinces. [ABSTRACT FROM AUTHOR]

There is an on-going debate on the environmental effects of genetically modified crops to which this paper aims to contribute. First, data on environmental impacts of genetically modified (GM) and conventional crops are collected from peer-reviewed journals, and secondly an analysis is conducted in order to examine which crop type is less harmful for the environment. Published data on environmental impacts are measured using an array of indicators, and their analysis requires their normalisation and aggregation. Taking advantage of composite indicators literature, this paper builds composite indicators to measure the impact of GM and conventional crops in three dimensions: (1) non-target key species richness, (2) pesticide use, and (3) aggregated environmental impact. The comparison between the three composite indicators for both crop types allows us to establish not only a ranking to elucidate which crop is more convenient for the environment but the probability that one crop type outperforms the other from an environmental perspective. Results show that GM crops tend to cause lower environmental impacts than conventional crops for the analysed indicators. [ABSTRACT FROM AUTHOR]

Sustainable development in its three dimensions – economic, social and environmental – has become a major concern on an international scale. The problem is global, but must be solved locally. Most of the world’s population lives in cities that act as centres of economic growth and productivity, but which – if they develop in the wrong direction – can cause social inequalities, or irreversibly harm the environment. Urban transport causes a number of negative impacts that can affect sustainability targets. The objective of this study is to propose an analysis of sustainability of urban passenger transport systems based on available indicators in most cities. This will serve to benchmark the practices of different cities and manage their transport systems. This work involves the creation of composite indicators (CI) to measure the sustainability of urban passenger transport systems. The methodology is applied to 23 European cities. The indicators are based on a benchmarking approach, and the evaluation of each aspect in each case therefore depends on the performance of the whole sample. The CI enabled us to identify which characteristics have the greatest influence on the sustainability of a city’s transport system, and to establish transport policies that could potentially improve its shortcomings. Finally, the cities are clustered according to the values obtained from the CIs, and thus according to the weaknesses and strengths of their transport systems. [ABSTRACT FROM AUTHOR]

This paper explores the relevance of the variables that define well-being and human progress and makes a quantitative inquiry into the validity of three of the well-known and well-documented composite indicators of well-being: the Human Development Index (HDI), the Legatum Prosperity Index (LPI) and the Happy Planet Index (HPI). After choosing the key variables that describe most of the objective and subjective dimensions of well-being, we perform cluster analysis to come up with an optimal grouping of countries based on their multidimensional performance on well-being. A comparison of the classifications obtained with the three indexes invalidates the HPI, confirms results obtained for the HDI, and validates for the first time the LPI as a reliable measure of well-being. The optimal cluster structure yields robust results, which correct the rank discrepancies between the HDI and LPI for a large number of countries. It also proves that a robust ranking of countries based on multidimensional well-being can be achieved with a relatively small number of variables, which mitigates the risk of including variables that are not reliable and/or not available for a significant number of countries. The fact that cluster analysis generates results based on similarities between observations and not on computed values based on the aggregation of variables helps overcome problems that may occur due to the distribution of variables and increases its value as a validation method. Therefore, validation results achieved through cluster analysis are more robust and help to achieve a good check of the validity and relevance of the composite indexes, provide an objective perspective that can guide policy-makers and the public in making a fair assessment of actual levels of well-being, and avoid unfounded claims that may overstate it and delay or postpone measures to increase it. [ABSTRACT FROM AUTHOR]

Highlights: [•] We construct a sustainability index using nonlinear aggregation, allowing for interactions among indicators. [•] 19 indicators are grouped in 3 pillars – economic, social and environmental sustainability. [•] Expert elicitation suggests that a majority of experts considers sustainability criteria as complementary to each other. [•] Countries that are ranked at higher (lower) positions have better (worse) outcomes in at least two sustainability pillars. [•] Robustness analysis shows countries’ rankings remain mainly the same across different combination of experts’ preferences. [Copyright &y& Elsevier]

Abstract: Considering the on-going strive towards new, alternative indicators to measure our societal development pathways, and the fact that policy indicators remain largely enigmatic with regard to their patterns of embeddedness in institutional decision-making processes, it appears necessary to work towards reducing our lack of understanding of their interactions with policy-making. In the present paper, we focus on exploring the significance of composite indicators for policy making in the particular policy environment of the EU-institutions. Our research is underpinned by the conviction that such indicators are not systematically used directly, but have an indirect influence on policy making that needs to be better understood. Our analytical framework – in order to analyse the ways in which composite indicators enter policy processes – is characterised by the distinction between the ‘use’ and the ‘influence’ of indicators on the one hand, and on the other hand between 3 types of factors: indicator factors, policy factors and user factors. Our empirical results show that while most of the academic attention and political debate around indicators has tended to focus on ‘indicator factors’, such quality attributes actually mattered relatively little in our setting as determinants of indicator influence. This rejects the idea that the robustness of evidence would lie exclusively in its technical quality and in the independence of its producer, and instead calls attention to the processes of evidence-construction. Simultaneously, ‘user factors’ (beliefs and representations of policy actors) and ‘policy factors’ (institutional context) were crucial as explanatory factors of the policy mechanics we identified. [Copyright &y& Elsevier]

Abstract: The non-parametric Data Envelopment Analysis approach is increasingly used to construct composite indicators for country performance monitoring, benchmarking, and policy evaluation in a large variety of fields. The flexibility in the definition of aggregation weights is praised as the method's most important advantage: DEA allows each evaluated country to look for its own optimal weights that maximize the composite indicator relative to the other countries. However, this flexibility also carries a potential disadvantage as it may allow countries to appear as a brilliant performer in a manner that is hard to justify: by ignoring or overemphasizing one or multiple of the judiciously selected performance indicators. To illustrate this issue of undesirable specialization in DEA-based evaluations, this paper compares the Environmental Performance Index (EPI) as computed by the optimistic and pessimistic version of the DEA-model as proposed by Zhou et al. (2007). Based on both computed composites, undesirable specialization in performance is identified. [Copyright &y& Elsevier]

Abstract: Sustainable tourism is being consolidated at an international level as an approach that should be used to make all types of tourism more environmentally, socially and economically beneficial. A common practice is to use an indicator system for designing and implementing tourism models that focuses on the sustainability approach. Indicators are considered as useful tools that allow tourism managers to diagnose the situation of the destination, and to identify and evaluate issues that require addressing to improve the level of sustainability of the tourist activities. However, these indicator systems usually do not provide a practical guide to establish how to interpret information and how to integrate it into the decision-making. In this paper we present an indicator system to evaluate sustainable tourism at cultural destinations. Likewise, we suggest a method based on goal programming to construct composite indicators. The definition of the indicator system and composite indicators is completed providing guidelines that establish how to use these tools in tourism sector planning. Specifically, we propose three basic practical uses for these tools: the formulation of general action plans at a regional level, the definition of short-term strategies for destinations and the establishment of destination benchmarking practices. Each practical use is illustrated using the case of cultural tourism destinations in the Andalusia region (Spain). [Copyright &y& Elsevier]

• We contribute an indicator of environmental performance in municipal solid waste treatment. • The environmental performance of European Union member states is empirically assessed. • Major differences in performance are found, with the best performers being Nordic and Central European countries. This paper proposes a measure of environmental performance in the treatment of municipal solid waste, which is defined as a ratio between a composite indicator of waste treated through environmentally desirable operations –recycling and recovery in our case study– and a composite indicator of waste treated through undesirable operations –landfill and incineration. Moreover, it contributes both overall and treatment-specific indicators of performance. Data Envelopment Analysis (DEA) techniques are used to compute the environmental performance indicators and they are illustrated with an empirical assessment of the environmental performance of the European Union-28 (EU-28) members in their treatment of municipal waste, with data for the year 2017. Our results point to a worryingly low average level of performance, with the best performers being mainly Nordic and Central European countries such as Sweden, Germany, Belgium, Finland and Denmark; at the opposite end of the spectrum, environmental performance in the treatment of waste is particularly low in most of the Eastern European countries that joined the EU-28 from the 2000s, and some Southern member states. The determinants of performance are also investigated, the main finding being a positive and statistically significant association between environmental performance in municipal waste treatment and the level of economic development. [ABSTRACT FROM AUTHOR]

• Administrative planning duties have to be considered in climate resilience assessment. • Machine learning tools provides means for empirical validation. • Metropolitan areas achieve a higher climate resilience compared to rural. • Life expectancy is a valid outcome for climate resilience validation. High uncertainty in the occurrence of extreme events and disasters have made resilience-building an imperative part of society. Resilience assessment is an important tool in this context. Resilience is multidimensional as well as place-, scale- and time-specific, which requires a comprehensive approach for measuring and analysing. In this regard, composite indicators are preferred, and extensive literature is available on resilience indices on all spatial and temporal scales as well as hazard-specific or multi-hazard related indicators. However, transparent, robust, validated and transferable metrics are still missing from the scientific discourse. Hence, the research follows a novel composite index development approach: First, to develop and operationalise climate resilience on the county level in the state of Baden-Württemberg, Germany; second, to develop multiple composite indices in order to assess the impact of the construction methodology to increase transparency and decrease uncertainty; third, validating the index by statistical as well as empirical data and machine learning models - which is a novel endeavour so far. The results underscored that the two-step inclusive validation of data-driven statistical analysis in combination with empirical data proved to be essential in developing the index during the selection and aggregation of indicators. The results also highlighted a lower climate resilience of rural regions compared to metropolitan regions despite their better environmental status. Overall, machine learning proved to be essential in understanding and linking indicators and indices to policy, resilience and empirical data. The research contributes to a better understanding of climate resilience as well as to the methodological construction of composite indicators. [ABSTRACT FROM AUTHOR]