Sustainable development
Identifying the links among urban climate hazards, mitigation and adaptation actions and sustainability for future resilient cities
Comprehensive and objective assessment methods need to be developed to create inclusive, safe, resilient and sustainable cities. Monitoring the evolution of sustainability and well-being in the cities is important for researchers implementing the UN 2030 Agenda. This research explores and analyzes the climate change hazards, adaptation- and mitigation actions and their implementation in 776 cities located in 84 different countries. The climate action co-benefits are supporting the achievement of sustainable development goals, which are comprehensively elaborated in this methodological development. The analyzes are carried out based on the continuously updated Carbon Disclosure Project database. An open source algorithm has been developed that represents the CDP database as a bit table and use frequent itemset mining for the identification of global patterns of climate hazards, mitigation- and adaptation actions and their co-benefits, therefore, this paper offers an exploratory analysis tool that is suitable for monitoring climate actions. The most frequently identified mitigation actions in cities were energy planting (1444 actions), and on-site renewable production (644), while the most common actions for adaptation were tree planting (283) and flood mapping (267). Regarding city size, 41% of large metropolitan areas plan to develop mass transit actions, while the separate collection of recyclables is typical in 85% of towns. 56.2% of CDP database actions support access to sustainable cities and communities goal (SDG11), 54.2% access to climate action goal (SDG13), and the emergence of affordable and clean energy (SDG7) and gender equality goal (SDG5) are below 5%.
Post date: 9 June 2023
Automated Analysis of the Interactions Between Sustainable Development Goals Extracted from Models and Texts of Sustainability Science
The design and monitoring of sustainable policies should rely on models that can handle complex and interconnected variables and subsystems of sustainability issues. Structuring knowledge has been identified as an essential first step in building models of sustainability science. Although it is known that all models yield a reduced view of the examined topic and no models can include all the variables that would make the representation closed and comprehensive, in the case of sustainability issues it is critical to synthesize as many critical aspects as possible that could have an impact on the studied problem. The key idea of our research is that strategic plans, sustainability reports and scientific studies reflect these variables, therefore, with the tools of text mining, the most important focus points and interactions can be determined. These key aspects and their connections can be represented by a network structure and compared to the subsystems of the dynamic models of sustainability to explore the deficiencies of the models or the lack of focus of the related policies and documentations. In the present work, the proposed methodology through the analysis of five strategical documents is demonstrated and the determined aspects with the structure of the famous World3 system dynamics model compared. The comparison highlighted the incomplete view of the original World3 model since certain topics were not critical issues whilst the World3 model was in development.
The Applicability of Big Data in Climate Change Research: The Importance of System of Systems Thinking
The aim of this paper is to provide an overview of the interrelationship between data science and climate studies, as well as describes how sustainability climate issues can be managed using the Big Data tools. Climate-related Big Data articles are analyzed and categorized, which revealed the increasing number of applications of data-driven solutions in specific areas, however, broad integrative analyses are gaining less of a focus. Our major objective is to highlight the potential in the System of Systems (SoS) theorem, as the synergies between diverse disciplines and research ideas must be explored to gain a comprehensive overview of the issue. Data and systems science enables a large amount of heterogeneous data to be integrated and simulation models developed, while considering socio-environmental interrelations in parallel. The improved knowledge integration offered by the System of Systems thinking or climate computing has been demonstrated by analysing the possible inter-linkages of the latest Big Data application papers. The analysis highlights how data and models focusing on the specific areas of sustainability can be bridged to study the complex problems of climate change.
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.
Focal points for sustainable development strategies—Text mining-based comparative analysis of voluntary national reviews
Countries have to work out and follow tailored strategies for the achievement of their Sustainable Development Goals. At the end of 2018, more than 100 voluntary national reviews were published. The reviews are transformed by text mining algorithms into networks of keywords to identify country-specific thematic areas of the strategies and cluster countries that face similar problems and follow similar development strategies. The analysis of the 75 VNRs has shown that SDG5 (gender equality) is the most discussed goal worldwide, as it is discussed in 77% of the analysed Voluntary National Reviews. The SDG8 (decent work and economic growth) is the second most studied goal, With 76 %, while the SDG1 (no poverty) is the least focused goal, it is mentioned only in 48 % of documents and the SDG10 (reduced inequalities) in 49 %. The results demonstrate that the proposed benchmark tool is capable of highlighting what kind of activities can make significant contributions to achieve sustainable developments.
Review and structural analysis of system dynamics models in sustainability science
As the complexity of sustainability-related problems increases, it is more and more difficult to understand the related models. Although tremendous models are published recently, their automated structural analysis is still absent. This study provides a methodology to structure and visualise the information content of these models. The novelty of the present approach is the development of a network analysis-based tool for modellers to measure the importance of variables, identify structural modules in the models and measure the complexity of the created model, and thus enabling the comparison of different models. The overview of 130 system dynamics models from the past five years is provided. The typical topics and complexity of these models highlight the need for tools that support the automated structural analysis of sustainability problems. For practising engineers and analysts, nine models from the field of sustainability science, including the World3 model, are studied in details. The results highlight that with the help of the developed method the experts can highlight the most critical variables of sustainability problems (like arable land in the Word 3 model) and can determine how these variables are clustered and interconnected (e.g. the population and fertility are key drivers of global processes). The developed software tools and the resulted networks are all available online.
Data-driven multilayer complex networks of sustainable development goals
This data article presents the formulation of multilayer network for modelling the interconnections among the sustainable development goals (SDGs), targets and includes the correlation based linking of the sustainable development indicators with the available long-term datasets of The World Bank, 2018. The spatial distribution of the time series data allows creating country-specific sustainability assessments. In the related research article “Network Model-Based Analysis of the Goals, Targets and Indicators of Sustainable Development for Strategic Environmental Assessment” the similarities of SDGs for ten regions have been modelled in order to improve the quality of strategic environmental assessments. The datasets of the multilayer networks are available on Mendeley.
Network Model-Based Analysis of the Goals, Targets and Indicators of Sustainable Development for Strategic Environmental Assessment
Strategic environmental assessment is a decision support technique that evaluates policies, plans and programs in addition to identifying the most appropriate interventions in different scenarios. This work develops a network-based model to study interlinked ecological, economic, environmental and social problems to highlight the synergies between policies, plans, and programs in environmental strategic planning. Our primary goal is to propose a methodology for the data-driven verification and extension of expert knowledge concerning the interconnectedness of the sustainable development goals and their related targets. A multilayer network model based on the time-series indicators of the World Bank open data over the last 55 years was assembled. The results illustrate that by providing an objective and data-driven view of the correlated variables of the World Bank, the proposed layered multipartite network model highlights the previously not discussed interconnections, node centrality measures evaluate the importance of the targets, and network community detection algorithms reveal their strongly connected groups. The results confirm that the proposed methodology can serve as a data-driven decision support tool for the preparation and monitoring of long-term environmental policies. The developed new data-driven network model enables multi-level analysis of the sustainability (goals, targets, indicators) and will make it possible to plan long-term environmental strategic planning. Through relationships among indicators, relationships among targets and goals can be modelled. The results show that sustainable development goals are strongly interconnected, while the 5th goal (gender equality) is linked mostly to 17th (partnerships for the goals) goal. The analysis has also highlighted the importance of the 4th (quality education).
Evaluating the interconnectedness of the sustainable development goals based on the causality analysis of sustainability indicators
Policymaking requires an in-depth understanding of the cause-and-effect relationships between the sustainable development goals. However, due to the complex nature of socio-economic and environmental systems, this is still a challenging task. In the present article, the interconnectedness of the United Nations (UN) sustainability goals is measured using the Granger causality analysis of their indicators. The applicability of the causality analysis is validated through the predictions of the World3 model. The causal relationships are represented as a network of sustainability indicators providing the opportunity for the application of network analysis techniques. Based on the analysis of 801 UN indicator types in 283 geographical regions, approximately 4000 causal relationships were identified and the most important global connections were represented in a causal loop network. The results highlight the drastic deficiency of the analysed datasets, the strong interconnectedness of the sustainability targets and the applicability of the extracted causal loop network. The analysis of the causal loop networks emphasised the problems of poverty, proper sanitation and economic support in sustainable development.
The analysed datasets, the results of the Granger-causality analyses of the UN sustainability indicators and the original version of the network, its node and edge lists and a Gephi version of the network is available here.
Data-driven comparative analysis of national adaptation pathways for Sustainable Development Goals
Since the declaration of Sustainable Development Goals (SDGs) in 2015, countries have begun developing and strategizing their national pathways for effective implementation of the 2030 Agenda. The sustainable development targets set out how the world’s nations must move forward so that sustainable development is not an ideal vision but a workable, comprehensive environmental, economic, and social policy. This work aims to analyze the state of progress towards achieving sustainable development goals for each country. In addition to the static presentation of the achievements that countries can present, the changes over time are also compared, allowing countries to be grouped according to the current states. A sophisticated SDG performance measurement tool has been developed to support this analysis, which automatically processes the entire UN Global SDG Indicators database with exploratory data analysis, frequent item mining, and network analysis supported. Based on the trend analysis of the percentiles, the values of the indicators achievable by 2030 are also derived. The analyzes were performed based on the time-series data of 1319 disaggregated official SDG indicators.
Most of the world countries have achieved the greatest success in SDG12 and SDG10 since the declaration of the 2030 Agenda. In the field of climate change (SDG13), 26 countries can count on significant achievements. However, SDG6, SDG2, and SDG1 face significant challenges globally, as they have typically seen minor progress in recent years. Examined at the indicator level, indicators 1.4.1, 5.6.2, 6.b.1, 10.7.2, and 15.4.2 improved in all countries of the world, while indicators 2.a.1, 9.4.1, 2.1.1, 2.1. and 12.b.1 have deteriorated predominantly. According to the forecast for 2030, Australia and the United States can reduce their per capita CO2 emissions, while some countries in Africa, Asia, and the Middle East are expected to increase their emissions.
