Measuring and evaluating SDG indicators with Big Earth Data

Polar regions are critical in achieving global sustainable development goals

As important components of global commons, environmental changes in polar regions are crucial to the local and global sustainability. However, they have received little attention in the current framework of sustainable development goals (SDGs). This study examines the impacts of climate change in polar regions, emphasizing the interconnectedness of these areas with other parts of the global system. Here we show that polar regions are a limiting factor in achieving global SDGs, similar to the "shortest stave" in Liebig's barrel, primarily due to the teleconnection effects of climate tipping elements. Proactive actions should ensure polar regions aren't left behind in achieving global SDGs. We proposed a specific SDG target and five indicators for the interconnected effect of the cryosphere on climate actions and incorporate considerations for Indigenous peoples in polar regions. With the right actions and strengthened global partnerships, polar regions can be pivotal for advancing global sustainable development.

Machine learning for a sustainable energy future

Energy production is one of the key enablers for human activities such as food and clean water production, transportation, telecommunication, education, and healthcare; however, it is also the main cause of global warming. Hence, sustainable energy is critical for most United Nations (UN) Sustainable Development Goals (SDGs), and it is directly targeted in SDG7. In this review, we analyze the potential role of machine learning (ML), another enabler technology, in sustainable energy and SGDs. We review the use of ML in energy production and storage as well as in energy forecasting and planning activities and provide our perspective on the challenges and opportunities for the future role of ML. Although there are strong challenges for both sustainable energy supply (like conflict between the urgent energy needs and global warming) and ML applications (like high energy consumption in ML applications and risk of increasing inequalities among people and nations), ML may make significant contributions to sustainable energy efforts and therefore to the achievement of SDGs through monitoring and remote sensing to collect data, planning the worldwide efforts and improving the performance of new and more sustainable energy technologies.

Revealing the contribution of mountain ecosystem services research to sustainable development goals: A systematic and grounded theory driven review

Ecosystem services are the bridge between people and nature, especially for mountains, which cover more than two thirds of the world's territory, and are able to provide a diversity of ecosystem services and are significant for the enhancement of human well-being. Understanding how mountain ecosystem services (MES) support The United Nations (UN) Sustainable Development Goals (SDGs) is critical to realizing effective benefits from mountain resources, yet the extent to which MES support the SDGs is currently unclear and needs to be further explored. This study systematically reviewed the current research works by using grounded theory. We searched the Web of Science platform for papers closely related to mountain ecosystem services (2008-2022) and obtained 2010 papers, and further streamlined the most representative 114 papers based on the direct correlation between typical mountains, ecosystem service, and SDGs in the literature. We then explored the relationship between MES and specific SDGs and focused on the most strongly linked goals. The study indicated: (1) 66 targets (39%) and 12 SDGs (71%) were found, and we categorized the linkages into three categories, benefit, synergize, benefit & synergize. SDG3, 11, 13, 15 are goals that most strongly link to the MES, Subclasses storage and soil conservation services of ES are the most studied; (2) There is a gap between research and specific SDGs, and we need to focus on specific goals with relevant MES that are poorly researched but emphasized in SDGs; (3) The extent and emphasis of attention to mountain ecosystem services varied globally across continental regions. Therefore, we summarized a sustainable management model for mountain features. Policy makers are advocated to use our recommendations as a reference based on the specific features of the local mountains in combination with the development aims.

A systematic study of interactions between sustainable development goals (SDGs) in Hainan Island

The 2030 Agenda for Sustainable Development issued by the United Nations is an important foundation for countries to achieve common economic, social and environmental development. Important progress has been made in the evaluation of the Sustainable Development Goals (SDGs) in Hainan Island; nevertheless, there is still a lack of understanding around the trade-offs and synergies between the SDGs. Studying the trade-offs and synergies between Hainan Island's sustainable development goals is of great significance for the coordinated development of these goals and the promotion of the construction of free trade ports. Therefore, based on the United Nations Sustainable Development Assessment System and the existing SDG indicator system on Hainan Island, this paper identifies and quantifies the trade-offs and synergies within and between SDGs and targets on the county scale. Based on the different impacts of different spatial, dimensional and geographical directions, the results show the following: (1) Hainan Province made good progress on multiple SDGs between 2010 and 2021. (2) The most significant synergies between SDGs exist between SDG1 (No Poverty) and SDG10 (Reduce Inequalities), while the most significant trade-offs exist between SDG2 (Zero Hunger) and SDG4 (Quality Education). (3) Obvious spatial characteristics in trade-offs and synergies exist, with the highest level of synergy being in the Haikou and Sanya Economic Circles and their surrounding areas, and in the central region of Hainan Island which has a higher level of trade-offs. (4) The synergistic effect between the SDG targets and indicators in Hainan is much greater than the trade-off effect: the four aspects of people's livelihood improvement, economic development, resource utilization and environmental quality all show synergistic effects in different regions.

Trusted artificial intelligence for environmental assessments: An explainable high-precision model with multi-source big data

Environmental assessments are critical for ensuring the sustainable development of human civilization. The integration of artificial intelligence (AI) in these assessments has shown great promise, yet the "black box" nature of AI models often undermines trust due to the lack of transparency in their decision-making processes, even when these models demonstrate high accuracy. To address this challenge, we evaluated the performance of a transformer model against other AI approaches, utilizing extensive multivariate and spatiotemporal environmental datasets encompassing both natural and anthropogenic indicators. We further explored the application of saliency maps as a novel explainability tool in multi-source AI-driven environmental assessments, enabling the identification of individual indicators' contributions to the model's predictions. We find that the transformer model outperforms others, achieving an accuracy of about 98% and an area under the receiver operating characteristic curve (AUC) of 0.891. Regionally, the environmental assessment values are predominantly classified as level II or III in the central and southwestern study areas, level IV in the northern region, and level V in the western region. Through explainability analysis, we identify that water hardness, total dissolved solids, and arsenic concentrations are the most influential indicators in the model. Our AI-driven environmental assessment model is accurate and explainable, offering actionable insights for targeted environmental management. Furthermore, this study advances the application of AI in environmental science by presenting a robust, explainable model that bridges the gap between machine learning and environmental governance, enhancing both understanding and trust in AI-assisted environmental assessments.

Artificial intelligence for geoscience: Progress, challenges, and perspectives

This paper explores the evolution of geoscientific inquiry, tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence (AI) and data collection techniques. Traditional models, which are grounded in physical and numerical frameworks, provide robust explanations by explicitly reconstructing underlying physical processes. However, their limitations in comprehensively capturing Earth's complexities and uncertainties pose challenges in optimization and real-world applicability. In contrast, contemporary data-driven models, particularly those utilizing machine learning (ML) and deep learning (DL), leverage extensive geoscience data to glean insights without requiring exhaustive theoretical knowledge. ML techniques have shown promise in addressing Earth science-related questions. Nevertheless, challenges such as data scarcity, computational demands, data privacy concerns, and the "black-box" nature of AI models hinder their seamless integration into geoscience. The integration of physics-based and data-driven methodologies into hybrid models presents an alternative paradigm. These models, which incorporate domain knowledge to guide AI methodologies, demonstrate enhanced efficiency and performance with reduced training data requirements. This review provides a comprehensive overview of geoscientific research paradigms, emphasizing untapped opportunities at the intersection of advanced AI techniques and geoscience. It examines major methodologies, showcases advances in large-scale models, and discusses the challenges and prospects that will shape the future landscape of AI in geoscience. The paper outlines a dynamic field ripe with possibilities, poised to unlock new understandings of Earth's complexities and further advance geoscience exploration.

Recent impacts of water management on dryland's salinization and degradation neutralization

Reducing soil salinization of croplands with optimized irrigation and water management is essential to achieve land degradation neutralization (LDN). The effectiveness and sustainability of various irrigation and water management measures to reduce basin-scale salinization remain uncertain. Here we used remote sensing to estimate the soil salinity of arid croplands from 1984 to 2021. We then use Bayesian network analysis to compare the spatial-temporal response of salinity to water management, including various irrigation and drainage methods, in ten large arid river basins: Nile, Tigris-Euphrates, Indus, Tarim, Amu, Ili, Syr, Junggar, Colorado, and San Joaquin. In basins at more advanced phases of development, managers implemented drip and groundwater irrigation and thus effectively controlled salinity by lowering groundwater levels. For the remaining basins using conventional flood irrigation, economic development and policies are crucial for establishing a virtuous circle of "improving irrigation systems, reducing salinity, and increasing agricultural incomes" which is necessary to achieve LDN.

A future for digital public goods for monitoring SDG indicators

Digital public goods (DPGs), if implemented with effective policies, can facilitate the realization of the United Nations Sustainable Development Goals (SDGs). However, there are ongoing deliberations on how to define DPGs and assure that society can extract the maximum benefit from the growing number of digital resources. The International Research Center of Big Data for Sustainable Development Goals (CBAS) sees DPGs as an important mechanism to facilitate information-driven policy and decision-making processes for the SDGs. This article presents the results of a CBAS survey of 51 respondents from around the world spanning multiple scientific fields, who shared their expert opinions on DPGs and their thoughts about challenges related to their practical implementation in supporting the SDGs. Based on the survey results, the paper presents core principles in a proposed strategy, including establishment of international standards, adherence to open science and open data principles, and scalability in monitoring SDG indicators. A community-driven strategy to develop DPGs is proposed to accelerate DPG production in service of the SDGs while adhering to the core principles identified in the survey.

Dynamic landscapes and the influence of human activities in the Yellow River Delta wetland region

The Yellow River Delta (YRD) wetland is one of the largest and youngest wetland ecosystems in the world. It plays an important role in regulating climate and maintaining ecological balance in the region. This study analyzes the spatiotemporal changes in land use, wetland migration, and landscape pattern from 2013 to 2022 using Landsat-8 and Sentinel-1 data in YRD. Then wetland landscape changes and the impact of human activities are determined by analyzing correlation between landscape and socio-economic indicators including nighttime light centroid, total light intensity, cultivated land area and centroid, building area and centroid, economic and population. The results show that the total wetland area increased 1426 km2 during this decade. However, the wetland landscape pattern tended to be fragmented from 2013 to 2022, with wetlands of different types interlacing and connectivity decreasing, and distribution becoming more concentrated. Different types of human activities had influences on different aspects of wetland landscape, with the expansion of cultivated land mainly compressing the core area of wetlands from the edge, the expansion of buildings mainly disrupting wetland connectivity, and socio-economic indicators such as total light intensity and the centroid mainly causing wetland fragmentation. The results show the changes of the YRD wetland and provide an explanation of how human activities effect the change of its landscape, which provides available data to achieve sustainable development goals 6.6 and may give an access to measure the change of wetland using human-activity data, which could help to adject behaviors to protect wetlands.

Sustainable development goals applied to digital pathology and artificial intelligence applications in low- to middle-income countries

Digital Pathology (DP) and Artificial Intelligence (AI) can be useful in low- and middle-income countries; however, many challenges exist. The United Nations developed sustainable development goals that aim to overcome some of these challenges. The sustainable development goals have not been applied to DP/AI applications in low- to middle income countries. We established a framework to align the 17 sustainable development goals with a 27-indicator list for low- and middle-income countries (World Bank/WHO) and a list of 21 essential elements for DP/AI. After categorization into three domains (human factors, IT/electronics, and materials + reagents), we permutated these layers into 153 concatenated statements for prioritization on a four-tiered scale. The two authors tested the subjective ranking framework and endpoints included ranked sum scores and visualization across the three layers. The authors assigned 364 points with 1.1-1.3 points per statement. We noted the prioritization of human factors (43%) at the indicator layer whereas IT/electronic (36%) and human factors (35%) scored highest at the essential elements layer. The authors considered goal 9 (industry, innovation, and infrastructure; average points 2.33; sum 42), goal 4 (quality education; 2.17; 39), and goal 8 (decent work and economic growth; 2.11; 38) most relevant; intra-/inter-rater variability assessment after a 3-month-washout period confirmed these findings. The established framework allows individual stakeholders to capture the relative importance of sustainable development goals for overcoming limitations to a specific problem. The framework can be used to raise awareness and help identify synergies between large-scale global objectives and solutions in resource-limited settings.

A bibliometric analysis of sustainable development goals (SDGs): a review of progress, challenges, and opportunities

The Sustainable Development Goals (SDGs) are a global appeal to protect the environment, combat climate change, eradicate poverty, and ensure access to a high quality of life and prosperity for all. The next decade is crucial for determining the planet's direction in ensuring that populations can adapt to climate change. This study aims to investigate the progress, challenges, opportunities, trends, and prospects of the SDGs through a bibliometric analysis from 2015 to 2022, providing insight into the evolution and maturity of scientific research in the field. The Web of Science core collection citation database was used for the bibliometric analysis, which was conducted using VOSviewer and RStudio. We analyzed 12,176 articles written in English to evaluate the present state of progress, as well as the challenges and opportunities surrounding the SDGs. This study utilized a variety of methods to identify research hotspots, including analysis of keywords, productive researchers, and journals. In addition, we conducted a comprehensive literature review by utilizing the Web of Science database. The results show that 31% of SDG-related research productivity originates from the USA, China, and the UK, with an average citation per article of 15.06. A total of 45,345 authors around the world have contributed to the field of SDGs, and collaboration among authors is also quite high. The core research topics include SDGs, climate change, Agenda 2030, the circular economy, poverty, global health, governance, food security, sub-Saharan Africa, the Millennium Development Goals, universal health coverage, indicators, gender, and inequality. The insights gained from this analysis will be valuable for young researchers, practitioners, policymakers, and public officials as they seek to identify patterns and high-quality articles related to SDGs. By advancing our understanding of the subject, this research has the potential to inform and guide future efforts to promote sustainable development. The findings indicate a concentration of research and development on SDGs in developed countries rather than in developing and underdeveloped countries.

Graphical abstract

Future land-use change and its impact on terrestrial ecosystem carbon pool evolution along the Silk Road under SDG scenarios

Sustainable development goals (SDGs) in the United Nations 2030 Agenda call for action by all nations to promote economic prosperity while protecting the planet. Projection of future land-use change under SDG scenarios is a new attempt to scientifically achieve the SDGs. Herein, we proposed four scenario assumptions based on the SDGs, including the sustainable economy (ECO), sustainable grain (GRA), sustainable environment (ENV), and reference (REF) scenarios. We forecasted land-use change along the Silk Road (resolution: 300 m) and compared the impacts of urban expansion and forest conversion on terrestrial carbon pools. There were significant differences in future land use change and carbon stocks, under the four SDG scenarios, by 2030. In the ENV scenario, the trend of decreasing forest land was mitigated, and forest carbon stocks in China increased by approximately 0.60% compared to 2020. In the GRA scenario, the decreasing rate of cultivated land area has slowed down. Cultivated land area in South and Southeast Asia only shows an increasing trend in the GRA scenario, while it shows a decreasing trend in other SDG scenarios. The ECO scenario showed highest carbon losses associated with increased urban expansion. The study enhances our understanding of how SDGs can contribute to mitigate future environmental degradation via accurate simulations that can be applied on a global scale.

Temporal and spatial changes in land use and ecosystem service value based on SDGs' reports: a case study of Dianchi Lake Basin, China

Understanding the impact of land use and ecosystem services on sustainable development goals is a key to achieving sustainable development goals (SDGs). Taking Dianchi Lake Basin as the research area, land use data from five periods, 2001, 2005, 2010, 2015, and 2019, were analyzed using the dynamic equivalent method to determine ecosystem service value (ESV) and hot spot analysis method to explore temporal and spatial changes in ESV in Dianchi Lake Basin. Three sustainable development goals, SDG15.1.1, SDG15.2, and SDG15.3.1, were selected to quantitatively analyze the impact of land use change in Dianchi Lake Basin. The results showed that (1) in the 20-year study period, the main land use types in Dianchi Lake Basin were forest land, cultivated land, construction land, and water area. In the land transfer, the largest amount of land transferred out is cultivated land, accounting for 35.50% of the total transferred out amount. It is transferred to construction land, resulting in significant expansion of construction land, nearly twice as much. (2) The SDG15.1.1 index of three forest land types in Dianchi Lake Basin showed a downward trend, and the total forest land decreased from 45.36 to 41.80%, with a cumulative decrease of 3.56%, of which 2.35% was caused by the transformation from open forest land to other land types. For watershed SDG15.2 and SDG15.3.1 indicators, all were degraded, but the degradation of high forest (SDG15.2) was the most obvious. (3) From 2001 to 2019, the total ESV in Dianchi Lake Basin initially decreased and then increased before decreasing again, with an overall decrease of 3.687 billion yuan. The ESV in the study area was high in the middle and low in the periphery, and the water area dominated by Dianchi Lake was the highest value area. (4) From 2005 to 2019, the spatial displacement relationship between cold and hot spots dominated by Dianchi Lake was corresponding and obvious, during which the ESV fluctuated violently. This study provides a basis for the sustainable development and ecological construction in typical urbanized watershed.