Assessing regional performance for the Sustainable Development Goals in Italy The monitoring report on progress towards the Sustainable Development Goals (SDGs) in a global context involves a large number of actors as it represents probably the biggest change that our society is implementing. Actions at all levels, from local, regional and national to the aggregation of multiple countries (e.g. EU 27) are needed to achieve a sustainable future. This work focuses on a national perspective (Italy) where multi-criteria decision analysis (MCDA) is used to measure current performance. A sustainability score for each region is calculated from a set of 175 indicators contained in all 17 SDGs. Additionally, sustainability scores are disaggregated along the three pillars – social (1–5, 10, 16, and 17), environmental (6, 13–15) and economic (7–9, 11, and 12). The results highlight the positive performance of northern regions and, in particular, of Trentino Alto Adige, which ranks first in the two considered scenarios. In addition, the relevance of territorial specificities emerges for which the analysis of individual SDGs shows different leading regions. It is noteworthy to highlight the performance of the environmental sub-group of SDGs in southern regions, in contrast to the social and economic sub-groups. Evidently, policy actions are needed to reduce the long-lasting North/South divide—yet the highlighted heterogeneous sustainability performance along the three dimensions calls for well targeted policy measures necessary to regain competitiveness at a European and global level, without compromising with environmental sustainability. The pursuit of sustainability goals is a feat shared by many researchers, policymakers, and other stakeholders because it is considered essential to improving the quality of human life while respecting the surrounding environment. At the end of the 2021 G7 summits, a strong commitment emerged to support developing countries to provide them with more, better, and faster funding to achieve the sustainability transition. In the context of science and technology studies, researchers efforts have growingly focused on the identification of policies that can foster such change1,2,3. This transition can be facilitated by acting on global carbon pricing, through either taxation or emissions trading, as it is considered the easiest instrument to negotiate and induce behavioural changes4.However, although the pandemic period has reignited the urgency to combat climate change, previous studies already highlighted the positive link between sustainability and resilience5. The Adoption of the 2030 Agenda for Sustainable Development and its Sustainable Development Goals (SDGs), targets and related indicators has further pushed towards the sustainable transition6,7.SDGs can be used to support policymaking and should be pursued holistically together8. Yet the same countries seem to prioritize certain SDGs at the expenses of others. ‘Such cherry-picking defies the integrated and indivisible nature of the SDGs, and could negatively impact overall progress on sustainable development globally’9,10. To pursue such holistic approach, specific actions are encouraged: the use of Industry 4.0 technologies, the dissemination of education 4.0 to poorer nations, the collaboration between developed and developing countries and the development of a globalized circular economy11. However, the SDGs lack an overarching goal and an effective aggregate indicator that monitors progress toward that goal12. The literature highlights the need for an aggregate indicator that assesses as much the contributions associated with each SDGs as the interactions between them. Some authors propose a sustainable well-being index (SWI) linked to the SDGs and a comprehensive dynamic model capable of estimating future effects as a function of implemented policies13. Other authors introduce a 5SEnSU model, defining the area of sustainability that is least represented by the SDGs: environmental and economic capital should receive more attention in terms of targets towards the 2030 Agenda14. Emphasis in the implementation of the SDGs varies by geographic area and this requires further study on the analysis of local contexts to develop a comparative analysis that highlights existing problems15,16.Furthermore, the literature places a great deal of emphasis also on rankings—the goal being to raise awareness and accountability among countries toward achieving SDGs17,18,19. Analyses show that a country’s ranking is highly dependent on the method and indicators chosen and that the methods used so far have the limitation of not considering interconnections20. These analyses can be conducted not only to a global level20, but also at the local level21.Building on previous studies that assessed which are the European countries that have moved faster towards the SDGs22, this study aims to analyse a specific country (Italy), monitoring strengths and weaknesses in pursuing SDGs at regional level and identify at the same time characteristics that bind regions within the same nation. As we believe, this objective is important not only in view of the 2030 Agenda but also in the achievement of the Next Generation EU target (which sees Italy playing a central role, receiving just under a third of the total available budget). This work therefore aims to meet three objectives: the definition of a methodology easy replicable in order to assess a sustainability score able to compare territorial performance. the measurement of the performance of individual regions towards the SDGs through a sustainability score obtained through a multi-criteria analysis; the identification of policies to be implemented to resolve gaps in order to facilitate a sustainability transition. The first objective of this work concerns the definition of a methodology able to measure sustainable performance considering multiple perspectives, with indicators characterized by different units of measurement. In this regard the MCDA methodology is employed, provided that it allows combining indicators with different units of measurement in an appropriate way. The regional ranking obtained through the MCDA methodology leads to the second objective of the study. Finally, the decomposition of the analysis into different levels provides useful indications to illustrate the third objective related to policy implications. Consequently, the MCDA acquires a key role in the proposed study.MCDA is a methodology well established in the scientific literature as well as in applied contexts, as it allows comparisons among multiple alternatives that may also be conflicting23. The MCDA provides for the calculation of a final value for each alternative in which this value is obtained as a product between the relevance of the criteria and the values that the criteria assume for each alternative24. The multi-criteria analysis consists of two distinct phases in which values and weights are calculated. The result of MCDA analysis is the calculation of a Sustainability Score (dimensionless), obtained aggregating both values and weights of the criteria.Indeed, a critical element in this analysis is the availability and acquisition of reliable data25. Different indicators have different units of measurement and the great advantage of MCDA is to cancel out such differences. In fact, the normalization of all values in the range 0–1 is obtained by identifying a maximum and a minimum value. The normalization concerns a homogeneous panel of data (relative to each individual indicator), thus making in this process both the size and the differences in numerical magnitude among the different indicators irrelevant. A brief description of the SDGs can be found in the supplementary material.The 17 SDGs are clustered into three groups in accordance with existing literature13,26 and resembling the three pillars of sustainability: the social group (1–5, 10, 16, and 17), the economic group (7–9, 11, and 12), and the environmental group (6, 13–15)—Fig. 1.The identification of input dataThe Italian National Institute of Statistics (ISTAT)—ISTAT is engaged in the production of statistical measures to monitor progress towards the SDGs—collects data for each SDG disaggregated at regional level27. SDGs are organized into a system of 244 indicators, with which the guidelines for sustainable development are outlined worldwide.Building on ISTAT data, we are able to identify 175 indicators (see Supplementary Tables 1–10)—Fig. 2; the remaining 69 indicators are not provided at regional level by ISTAT and therefore are not included in this analysis. Of the available indicators 161 are ready to be used, while 14 need to be normalized according to population. It should be noted that 5 indicators were just doubling information and therefore were removed from our analysis. In the case of missing data, we resorted to other reliable statistical source, whenever available. All data used in this work are available in Supplementary Material.Figure 1Subdivision of indicators.Definition of scoring criteriaThe assessment of values for each alternative was comprised of 175 input data equal to the number of indicators (criteria). Since the units of measurement were not the same, it was necessary to perform normalization. This approach assigned 1 to the best performing value for each individual alternative (region) and 0 to the least performing24. All the regions with an intermediate performance were assigned a value between 0 and 1. It should be mentioned that the desired performance of different indicators bears different slopes, since in some cases an increase is to be preferred (e.g. Households’ satisfaction rate with respect to the continuity of the service of electricity supply), while in others a decrease is to be preferred (e.g. Housing cost overburden rate).Definition of weighting factorsThe relevance of the criteria is a complex issue since covered sustainability goals are broad in scope and substantially heterogeneous. Hence, it is difficult to identify an objective key for which there is a convergence of thought. Since these are indicators that collectively and holistically points towards sustainability, it is considered inappropriate to prioritise one indicator over another. Bearing this in mind, and considering the need to aggregate the selected set of indicators, the following two methods are proposed: Equal weight among SDGs (EWG) scenario. Equal weight among indicators (EWI) scenario. The first considers that all 17 SDGs have the same relevance regardless of the number of indicators that populate the individual SDG. Figure 2 highlights that we go from SDGs 13–14 having 3 indicators to SDG 3 having 28 indicators. Therefore, this scenario has the limitation of penalizing those criteria that are in larger groups. An alternative approach is therefore be to consider all critical indicators regardless of which SDG they belong to.Definition of sustainability scoreThis score is calculated for each alternative, which is represented by the N Italian Regions (IR). Sustainability score is calculated according to the approach proposed in Eqs. (1) and (2) in the EWG scenario and Eqs. (3) and (4) in the EWI scenario:$${mathrm{Sustainability,, Score}}_{mathrm{IR},mathrm{SDG}}={sum }_{mathrm{I}=1}^{{mathrm{N}}_{mathrm{I},mathrm{SDG}}}{mathrm{NRV}}_{mathrm{IR},mathrm{I}}/{mathrm{N}}_{mathrm{I},mathrm{SDG}}$$$${mathrm{Sustainability ,,Score}}_{mathrm{IR}}={sum }_{mathrm{SDG}=1}^{{mathrm{N}}_{mathrm{SDG}}}{mathrm{Sustainability,, Score}}_{mathrm{IR},mathrm{SDG}}$$$${mathrm{Sustainability,, Value}}_{mathrm{IR},mathrm{SDG}}={sum }_{mathrm{I}=1}^{{mathrm{N}}_{mathrm{I},mathrm{SDG}}}{mathrm{NRV}}_{mathrm{IR},mathrm{I}}$$$${mathrm{Sustainability,, Score}}_{mathrm{IR}}={sum }_{mathrm{SDG}=1}^{{mathrm{N}}_{mathrm{SDG}}}{mathrm{Sustainability,, Value}}_{mathrm{IR},mathrm{SDG}}/{mathrm{N}}_{mathrm{I},mathrm{SDG}}$$in which NRVIR,I is the normalized value of the I indicator for each IR region, NI,SDG is the number of indicators for each SDG and NSDG is the number of SDGs.In particular, the weight of the 175 indicators will be different in the two scenarios. So if we take one of the 9 indicators associated with SDG1 it has an intermediate weight of 0.111, while it is 0.1667 for one of the 6 indicators in SDG2. These weights are then aggregated across all 17 SDGs, resulting in the overall weights of 0.0065 and 0.0098 within the EWG scenario, respectively. In contrast, in the EWI scenario, the two indicators (taken earlier as examples) have both an overall weight of 0.0057.In addition, in order to identify any common characteristics, regional data are grouped into three macro-areas: North—Valle d’Aosta, Piemonte, Lombardia, Liguria, Trentino Alto Adige, Veneto, Friuli Venezia Giulia and Emilia Romagna. Center—Toscana, Umbria, Marche and Lazio. South—Abruzzo, Molise, Campania, Puglia, Basilicata, Calabria, Sardegna and Sicilia. The results obtained are based on the 3500 observations (175 indicators × 20 regions) and through normalization values were made comparable to each other. With regard to the weights, two different scenarios have been identified and by aggregating all these data, it is possible to obtain a sustainability score for each Italian region.Supplementary Tables 11–13 present the results for the EWG scenario in which the individual SDGs are normalized in the interval 0–1. Looking at these results at a glance, none of the SDGs reaches its maximum theoretical value of 1; the highest scores reached ar
https://www.nature.com/articles/s41598-021-03635-8
Assessing regional performance for the Sustainable Development Goals in Italy
