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An overview of the "COINr" R package, which is for building and analysing composite indicators.
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... of these functions allow any other function to be passed to them, allowing more complex types of normalisation and aggregation. Here, the code simply uses the "min-max" normalisation method (scaling indicators onto the [0,100] interval), and aggregates using the weighted arithmetic mean, following the hierarchical structure and weights specified in the iMeta argument of new_coin( We may also visualise the same results using a bar chart -here we see how countries rank on the "connectivity" sub-index (see Figure 1). ...
Citations
... The pyDecision library (Pereira et al., 2024) offers a large collection of MCDA methods and allows users to compare outcomes of different methods interactively, thanks to integration with ChatGPT. In R, the package COINr enables users to develop CIs with all standard operations, including criteria selection, data treatment, normalization, aggregation, and sensitivity analysis (Becker et al., 2022). Other packages, such as compind, focus specifically on weighting and aggregation (Fusco et al., 2018), while MATLAB tools like CIAO (Lindén et al., 2021) offer specialized capabilities for parts of CI development. ...
... In the evaluation of hypotheses, the significance of the association between variables (Table 8) was assessed using t-test analysis and path coefficient p values. To ensure robustness, the authors employed a nonparametric approach with the bootstrap technique to examine the precision of the hypotheses (Becker et al. 2022). It is evident that the majority of hypotheses are validated, as indicated by t-test analysis values exceeding 2.3 and p values below 0.05 (Rasoolimanesh et al. 2016). ...
This study explores the intricate associations between climate change anxiety, sustainable trust and three aspects of sustainable consumption behaviors (quality of life, care for environment and care for future generations) among consumers in Vietnam and Italy, with a focus on the moderating role of nature connectedness and hyperopia psychology. Employing an exploratory sequential mixed‐methods approach that integrates both qualitative and quantitative techniques, the study initially conducted 20 in‐depth interviews to gain insights into the interplay between climate change‐related factors, trust, and sustainable consumer behaviors, which helped to formulate the quantitative research framework. Subsequently, a quantitative approach was utilized, gathering 681 online surveys from Vietnamese and Italian consumers, with the data assessed through structural equation modeling using SmartPLS software. This study is the first to reveal crucial theoretical insights into how climate change anxiety and sustainable trust, affected by climate change risk perception and climate change knowledge, impact three aspects of sustainable consumption behaviors: quality of life, care for the environment, and care for future generations. These effects are positively moderated by hyperopia and nature connectedness, providing profound understanding of consumer behavior between the two regions within sustainable consumption amid the urgent global climate change crisis. Scholars benefit from an enriched understanding of consumer behaviors amidst climate change anxiety, while policymakers, businesses, and advocates gain actionable insights to drive effective climate mitigation and adaptation strategies, which contributes to advancing the United Nations' Sustainable Development Goals.
... All steps were carried out using R version 4.2.1 (R Core Team 2022) and the COINr library v.1.1.14 (Becker et al. 2022). ...
... Uncertainty and sensitivity analyses are conducted using R 4.3.1 (R Core Team 2023), RStudio (R Studio Team 2020) and the COINr package (Becker et al. 2022), which enables the construction and analysis of composite indicators. First, the methodology of constructing the SETI was implemented in R for weighting and aggregation steps, using the built-in tools to conduct the uncertainty and sensitivity analyses following the methodology described below. ...
... The methods used to define and calculate the FOBI are provided below following JRC and OECD's 'ideal sequence' of steps. All data preparation and analyses were carried out in R (R Core Team 2023), using the COINr package for aggregation of the index (Becker et al 2022). For simplicity, results are provided for England only. ...
... These results informed the final metric groupings and eliminations of metrics from aggregation into the index. Decisions were made to achieve significant positive correlations (0.3 ⩽ ρ < 0.92) within every grouping (e.g. between metrics within Level 2: Local Condition Subindex (figure 2)), whilst ensuring weaker correlations occurred between groupings (e.g. between Level 2: Local Condition Subindex and Level 2: Landscape Connectivity Subindex metrics) (Becker et al 2022, Casabianca et al 2022. Metrics were also checked to ascertain that they retained a positive influence on the composite indices they fed into at all levels of the index (rather than becoming 'silent') (Casabianca et al 2022, Freni-Sterrantino et al 2022). ...
Public forest agencies are obligated to take steps to conserve and where possible enhance biodiversity, but they often lack information and tools that support and evidence their decision making. To help inform and monitor impact of management actions and policies aimed at improving forest biodiversity, we have co-developed a quantitative, transparent and repeatable approach for assessing the biodiversity potential of the United Kingdom's (UK) publicly owned forests over space and time. The FOrest Biodiversity Index (FOBI) integrates several forest biodiversity indicators or 'metrics' , which characterise management-sensitive woodland and landscape features associated with biodiversity. These are measured or modelled annually using spatially comprehensive forest survey data and other well-maintained spatial environmental datasets. Following metric normalisation and a correlation analysis, a statistically robust selection of these metrics is aggregated using a hierarchical procedure to provide composite index scores. The FOBI metric and index results are provided for every individual public forest, and can be summarised across any reporting region of interest. Compared to existing indicators that rely on sample-based forest data, the results thus better support decisions and obligations at a range of scales, from locally targeted action to national, long-term biodiversity monitoring and reporting. We set out how the FOBI approach and associated bespoke online interfaces were co-developed to meet public forest agency needs in two constituent countries of the UK (England and Scotland), whilst providing a conceptual framework that can be adapted and transferred to other geographic areas and private forests. Example results are reported for England's public forests for four annual timestamps between 2014 and 2021, which indicate improvements to the biodiversity potential of public forests and surrounding landscapes over this time via increases in their diversity, extent, condition and connectivity.
... Existing software allows for analyzing the sensitivity of the composite indicator scores [34], evaluating the implicit weights of sub-indicators [35], applying different methods (e.g., Benet of the Doubt, Principal Component Analysis, and Mazziotta-Pareto Index) for building composite indicators [36], selecting sub-indicators, imputing data, aggregating sub-indicators and analyzing the sensitivity of the composite indicator scores [37], analyzing the relevance of sub-indicators [38], building composite indicators based on multiple combinations of normalization and aggregation functions, evaluating the results robustness [39], and considering spatial dependence in building a composite indicator [40]. Patented software focuses on building the composite indicator. ...
... The software offers decision-makers and researchers versatility and flexibility in building Another innovation of S-CI-OWA is measuring the quality of the composite indicators. These quality measurements are a remarkable advance concerning existing software [34][35][36][37][38][39][40][41][42] that mostly measures ranking uncertainty. The possibility of analyzing ranking uncertainty, the proportion of atypical measurements, and the composite indicator's informational, discriminating, and explanatory power further increases the potential impact of S-CI-OWA as it assists decision-makers and researchers in analyzing the usability of results in their decisions. ...
... They allow measuring the quality of composite indicators built by any method. The proposed software is a pioneer in bringing together these quality measures in a single place, but the possibility of incorporating it into other software [36][37][38][39][40] is an equally relevant contribution. The software allows decision-makers and researchers to direct the composite indicator scores up or down, allowing them to emphasize subindicators of greater or lesser value and define the intensity of this emphasis by regulating the compensation levels between sub-indicators. ...
Composite indicators are one-dimensional measures that help decision-makers understand complex realities. Existing software builds composite indicators using different methods, also providing a measure of the stability of their structure. Considering this, it is possible to point out that there is a gap for software that provides more quality measures and composite indicators capable of guiding decision-makers on their usability. The software for building and measuring the quality of composite indicators using Ordered Weighted Averaging, so-called SCI-OWA, fills this gap. The S-CI-OWA is based on Ordered Weighted Averaging, which assigns weights according to the input value, solving hierarchical evaluation problems considering the fuzzy nature of scoring, weighting, and aggregation operations. The S-CI-OWA offers three new features over existing software: one, versatility in driving composite indicator scores up or down; two, flexibility in defining this intensity of emphasis, allowing more positive or negative indicators (sub-indicators) to be highlighted in decision-making units; three a broader set of quality measures of composite indicators, notably: ranking uncertainty, ratio of atypical measurements, discriminating, explanatory and informational power.
... The gpindex package (Martin 2023) computes price indexes, and the fundiversity package (Grenié and Gruson 2023) computes functional diversity indexes for ecological study. The package COINr (Becker et al. 2022) is more ambitious, making a start on following the broader guidelines in the OECD handbook to construct, analyze, and visualize composite indexes. ...
Indexes are useful for summarizing multivariate information into single metrics for monitoring, communicating, and decision-making. While most work has focused on defining new indexes for specific purposes, more attention needs to be directed towards making it possible to understand index behavior in different data conditions, and to determine how their structure affects their values and variation in values. Here we discuss a modular data pipeline recommendation to assemble indexes. It is universally applicable to index computation and allows investigation of index behavior as part of the development procedure. One can compute indexes with different parameter choices, adjust steps in the index definition by adding, removing, and swapping them to experiment with various index designs, calculate uncertainty measures, and assess indexes' robustness. The paper presents three examples to illustrate the pipeline framework usage: comparison of two different indexes designed to monitor the spatio-temporal distribution of drought in Queensland, Australia; the effect of dimension reduction choices on the Global Gender Gap Index (GGGI) on countries' ranking; and how to calculate bootstrap confidence intervals for the Standardized Precipitation Index (SPI). The methods are supported by a new R package, called tidyindex.
... However, the index framework can be misleading in the absence of transparent indicator development methods (Acosta et al., 2021). Thus, best practices for developing composite indicators from OECD and JRC (2008), Acosta et al. (2019); , and Becker et al. (2017Becker et al. ( , 2022 were used and adapted to normalize and aggregate relevant but diverse and complex indicators into a common unit for measurement and assessment. ...
... While green growth is gaining ground, theoretically and practically, as vital pathways toward sustainable development, its measurement also makes headway. In the last decade, green growth indices, which use composite indices to assess, rank, and compare complex multidimensional concepts that are not immediately measurable, have gained popularity (OECD & JRC, 2008;Becker et al., 2017Becker et al., , 2022. Prominent international organizations developed a plethora of green growth index as sustainable development indicators (Acosta et al., 2019;AfDB, 2014;GGKP, 2013;Jha et al., 2018;OECD, 2017aOECD, , 2023PAGE, 2017;Tamanini & Valenciano, 2016;UNESCAP, 2013;World Bank, 2012). ...
Green growth gained traction as a global climate change strategy and pathway toward sustainable development. China's green growth has been on the rise since the turn of the century, yet it is little understood in the context of its provinces. Previous studies focus on ranking green growth across countries and regions, not on assessing individual provinces over time. This study employs systems thinking and constructs an index framework to assess the environmental, economic, and social dimensions of green growth as a pathway toward sustainable development in Qinghai on the Qinghai‐Tibet Plateau. The study finds that green growth has steadily increased between 2000 and 2021 despite a volatile growth rate. The 10th–13th Five‐Year Plans showed similar trends. Short‐term green growth performance fluctuated in its dimensions and pillars, while long‐term performance increased steadily. Qinghai is well‐positioned to achieve sustainable development and build a circular economy. The study further discusses sustainable policy implications.
... In what follows, we report the correlations between indicators in the same subindex, between indicators and their relative aggregates, and finally between dimensions, subindices and the C3 Index. The analysis results and figures presented in this section (Section 3.2) were derived from the COINr package developed by JRC-COIN (Becker et al., 2022). ...
The Cultural and Creative Cities Monitor (hereafter ‘Monitor’) is a monitoring and benchmarking tool first developed in 2017 to allow European cities to compare and contrast their areas of excellence and improvement in terms of culture and creativity. Since its first launch, the Monitor has served as a guidance tool for policy makers at local, national and European level.
The 2023 update of the Monitor provides a methodological improvement and a revision of the selected indicators, so to ensure the tool remains reliable and coherent, thus enabling meaningful comparisons of cities’ performance over time. The new results are available for three different reference years, with a focus on the most recent, 2019. These latest results provide a snapshot of the situation just before the COVID-19 pandemic.
An updated statistical assessment of the Monitor provided in this report allows the user to use the Monitor consciously, encouraging an informed use of the data and monitoring tools provided.
The update has also been applied to the online platform which, on top of the pre-existing tools, also allows users to compare and contrast the performance of 196 European cities at three different points in time.
... COINr includes several functions used for building/analyzing composite indicators, including normalization methods, either for all indicators or for each individually; weighting using manual, PCA weights, or correlation methods; and several aggregation methods for aggregating indicators at different aggregation level. COINr includes detailed indicator statistics, visualization tools, and data imputation methods [32]. ...
Smart mobility systems offers solutions for traffic congestion, transport management, emergency, and road safety. However, the success of smart mobility lies in the availability of intelligent transportation infrastructure. This paper studied smart mobility systems in three Asia-Pacific countries (South Korea, Singapore, and Japan) to highlight the major strategies leading their successful journey to become smart cities for aspiring countries, such as the Kingdom of Saudi Arabia (KSA), to emulate. A robust framework for evaluating smart mobility systems in the three countries and Saudi Arabia was developed based on the indicators derived from the smart mobility ecosystem and three major types of transport services (private, public, and emergency). Sixty indicators of smart mobility systems were identified through a rigorous search of the literature and other secondary sources. Robots, drones, IoT, 5G, hyperloop tunnels, and self-driving technologies formed part of the indicators in those countries. The study reveals that the three Asia-Pacific countries are moving head-to-head in terms of smart mobility development. Saudi Arabia can join these smarter countries through inclusive development, standardization, and policy-driven strategies with clear commitments to public, private, and research collaborations in the development of its smart mobility ecosystem. Moreover, cybersecurity must be taken seriously because most of the smart mobility systems use wireless and IoT technologies, which may be vulnerable to hacking, and thus impact system safety. In addition, the smart mobility system should include data analytics, machine learning, and artificial intelligence in developing and monitoring the evaluation in terms of user experience and future adaptability.
