Evaluating the Interconnectedness of the Sustainable Development Goals Based on the Causality Analysis of Sustainability Indicators

Acosta L, Lee S, Nzimenyera I, Sebestyén V, Abonyi J. Network science and explainable AI-based life cycle management of sustainability models

Model-based assessment of the potential impacts of variables on the Sustainable Development Goals (SDGs) can bring great additional information about possible policy intervention points. In the context of sustainability planning, machine learning techniques can provide data-driven solutions throughout the modeling life cycle. In a changing environment, existing models must be continuously reviewed and developed for effective decision support. Thus, we propose to use the Machine Learning Operations (MLOps) life cycle framework. A novel approach for model identification and development is introduced, which involves utilizing the Shapley value to determine the individual direct and indirect contributions of each variable towards the output, as well as network analysis to identify key drivers and support the identification and validation of possible policy intervention points. The applicability of the methods is demonstrated through a case study of the Hungarian water model developed by the Global Green Growth Institute. Based on the model exploration of the case of water efficiency and water stress (in the examined period for the SDG 6.4.1 & 6.4.2) SDG indicators, water reuse and water circularity offer a more effective intervention option than pricing and the use of internal or external renewable water resources.

A natural language processing model for supporting sustainable development goals: translating semantics, visualizing nexus, and connecting stakeholders

Sharing successful practices with other stakeholders is important for achieving SDGs. In this study, with a deep-learning natural language processing model, bidirectional encoder representations from transformers (BERT), the authors aimed to build (1) a classifier that enables semantic mapping of practices and issues in the SDGs context, (2) a visualizing method of SDGs nexus based on co-occurrence of goals (3) a matchmaking process between local issues and initiatives that may embody solutions. A data frame was built using documents published by official organizations and multi-labels corresponding to SDGs. A pretrained Japanese BERT model was fine-tuned on a multi-label text classification task, while nested cross-validation was conducted to optimize the hyperparameters and estimate cross-validation accuracy. A system was then developed to visualize the co-occurrence of SDGs and to couple the stakeholders by evaluating embedded vectors of local challenges and solutions. The paper concludes with a discussion of four future perspectives to improve the natural language processing system. This intelligent information system is expected to help stakeholders take action to achieve the sustainable development goals.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11625-022-01093-3.

Comprehensible Visualization of Multidimensional Data: Sum of Ranking Differences-Based Parallel Coordinates

A novel visualization technique is proposed for the sum of ranking differences method (SRD) based on parallel coordinates. An axis is defined for each variable, on which the data are depicted row-wise. By connecting data, the lines may intersect. The fewer intersections between the variables, the more similar they are and the clearer the figure becomes. Therefore, the visualization depends on what techniques are used to order the variables. The key idea is to employ the SRD method to measure the degree of similarity of the variables, establishing a distance-based order. The distances between the axes are not uniformly distributed in the proposed visualization; their closeness reflects similarity, according to their SRD value. The proposed algorithm identifies false similarities through an iterative approach, where the angles between the SRD values determine which side a variable is plotted. Visualization of the algorithm is provided by MATLAB/Octave source codes. The proposed tool is applied to study how the sources of greenhouse gas emissions can be grouped based on the statistical data of the countries. A comparison to multidimensional scaling (MDS)-based ordering is also given. The use case demonstrates the applicability of the method and the synergies of the incorporation of the SRD method into parallel coordinates.

Italy versus Other European Countries: Sustainable Development Goals, Policies and Future Hypothetical Results

The topic of sustainable development has become increasingly central to the international community. In 2015, the UN approved the 2030 Agenda, an action plan aimed at pursuing sustainable development. The founding elements of the 2030 Agenda are the 17 Sustainable Development Goals (SDG) that refer to different areas of development. The objective of this study is to determine the state of implementation of the SDGs in Italy and to understand to what extent the country will be able to reach European standards in 2030 under current policies. To this end, a quantitative analysis was carried out which, thanks to the use of official statistics and the FORECAST.ETS function, made it possible to identify the value that the indicators will have in 2030. In addition, the dynamic index methodology was applied to measure the degree of implementation of the SDGs between two different historical periods: 2018 and 2030. The analyses carried out shows that Italy needs to take urgent measures to meet its commitment to the 2030 Agenda. The study offers one of the first insights into the implementation of the 2030 Agenda as, in addition to analyzing the country’s performance, it examines the pursuit of the SDGs within the country itself. It is therefore believed that the results may be of interest to governments, experts, and academics.

The intertwining of world news with Sustainable Development Goals: An effective monitoring tool

This study aims to bring about a novel approach to the analysis of Sustainable Development Goals (SDGs) based solely on the appearance of news. Our purpose is to provide a monitoring tool that enables world news to be detected in an SDG-oriented manner, by considering multilingual as well as wide geographic coverage. The association of the goals with news basis the World Bank Group Topical Taxonomy, from which the selection of search words approximates the 17 development goals. News is extracted from The GDELT Project (Global Database of Events, Language and Tone) which gathers both printed as well as online news from around the world. 60 851 572 relevant news stories were identified in 2019. The intertwining of world news with SDGs as well as connections between countries are interpreted and highlight that even in the most SDG-sensitive countries, only 2.5% of the news can be attributed to the goals. Most of the news about sustainability appears in Africa as well as East and Southeast Asia, moreover typically the most negative tone of news can be observed in Africa. In the case of climate change (SDG 13), the United States plays a key role in both the share of news and the negative tone. Using the tools of network science, it can be verified that SDGs can be characterized on the basis of world news. This news-centred network analysis of SDGs identifies global partnerships as well as national stages of implementation towards a sustainable socio-environmental ecosystem. In the field of sustainability, it is vital to form the attitudes and environmental awareness of people, which strategic plans cannot address but can be measured well through the news.

Deciphering the scientific literature on SDG interactions: A review and reading guide

The 2030 Agenda includes 17 overarching Sustainable Development Goals (SDGs). These are integrated in nature, and a principle of indivisibility should guide their implementation. Yet, the 2030 Agenda itself does not provide guidance on what indivisibility means in practice, how the SDGs interact, or on how to assess these interactions. The fast-emerging field of what could be referred to as SDG interaction studies seeks to provide such guidance, but as of yet there is no general agreement on what it means to take an integrated approach to the SDGs. Hence, navigating the diverse research landscape on SDG interactions might prove challenging. This paper aims to decipher the literature on SDG interactions by providing an overview of the current research, based on a sample of 70 peer-reviewed articles. The review explores four themes in SDG interaction research by mapping: (i) policy challenges typically addressed, (ii) ways in which SDG 'interactions' have been conceptualized, (iii) data sources used, and (iv) methods of analysis frequently employed. Research gaps are identified, where perspectives largely missing include policy innovation, and integrated monitoring and evaluation. Further, few studies consider actor interactions, account for geographic spill-overs, analyze SDG indicator interactions, employ participatory methods, or take a whole-systems approach to the 2030 Agenda. Failing to address these gaps could lead to inefficient SDG implementation and delay goal attainment. Another contribution of the paper is a reading guide, proposing a way to decipher the literature along the themes emerging from the review, and offering a structure to code future papers.

A joint dataset of official COVID-19 reports and the governance, trade and competitiveness indicators of World Bank group platforms

The presented cross-sectional dataset can be employed to analyze the governmental, trade, and competitiveness relationships of official COVID-19 reports. It contains 18 COVID-19 variables generated based on the official reports of 138 countries (European Centre for Disease Prevention and Control, 2020 [1] and Beltekian et al. [2]), as well as an additional 2203 governance, trade, and competitiveness indicators from the World Bank Group GovData360(World Bank Group, 2020 [3]) and TCdata360(World Bank Group, 2020 [4]) platforms. From these platforms, only annual indicators from 2015 and later were collected, and their missing values were replaced with previous annual values, in descending order by year, until 2015. During preprocessing, indicators (columns) were filtered out when the ratio of missing values exceeded 50%. Then, the same filtration was applied for the ratio of missing values above 25% in the case of countries (rows). Finally, duplicated variables were removed from the dataset. As a result of these steps, the missing value rate of the employed indicators was reduced to 4.25% on average. In addition to the database, the Kendall rank correlation matrix is provided to facilitate subsequent analysis. The dataset and the correlation matrix can be updated and customized with an R Notebook file, which is also available publicly in Mendeley Data (Kurbucz, 2020 [5]).

Implementation of the 2030 Agenda Sustainable Development Goals in Spain

This paper examines the implementation status of sustainable development goals (SDG) in Spain and explores the extent to which the country will be able to meet European standards in sustainability by the year 2030 within the current regulation and praxis. Based on data retrieved from official statistics supplied by Eurostat for a set of indicators useful to monitor the goals our calculations prognosticate whether Spain will reach the European Union average values. The display of each relevant indicator is provided, as well as discussion on their evolution and some recommendations for an effective implementation of SDG on the mid-term, notwithstanding the peculiar political and socio-economic situation in the country. The study proves that Spain needs to adopt urgent regulatory measures and public policies in order to fulfill its commitment to the 2030 Agenda. Otherwise, if the ongoing trend continues, most of the Spanish indicators will not reach the European average values in the overwhelming majority of the goals, including areas as relevant as the struggle for education or environment.