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The United Nations 2030 Agenda for Sustainable Development and the Sustainable Development Goals (SDG’s) presents a roadmap and a concerted platform of action towards achieving sustainable and inclusive development, leaving no one behind, while preventing environmental degradation and loss of natural resources. However, population growth, increased...
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... outcome of the transition potential modelling is a series of transition potential maps, describing the suitability for each of the 12 major transitions included in the submodels. These maps can be seen in Figure 8. Figure 9 below shows the actual and the predicted land cover map for 2019. The actual 2019 land cover map was created using Landsat data composites from the years 2017-2019, applying the process described in Section 3.1.3. ...
Context 2
... visual inspection indicates that the predicted land cover map, overall, looks fairly similar to the actual land cover map, however there are localised discrepancies where the model failed to predict changes/persistence, for example, in the mid-west where the simulation predicted cropland to replace large open land areas, when in actuality it did not. Figure 9 below shows the actual and the predicted land cover map for 2019. The actual 2019 land cover map was created using Landsat data composites from the years 2017-2019, applying the process described in Section 3.1.3. ...
Citations
... For example, the group has utilized satellite data to monitor terrestrial ecosystems such as settlements, forests, water bodies, and agricultural lands. Google Earth Engine and Microsoft planetary computers have been utilized in the development of interactive tools for monitoring land cover/use changes, urbanization, deforestation, and wildfires' susceptibility mapping [21,22]. In addition, the monitoring of public health was supported with a data-driven approach that explored the spatiotemporal patterns of the COVID-19 pandemic in Europe at different levels, making use of machine learning methods for exploring the relationship between cityscape characteristics, demographics, climatic factors, and the pandemic outbreak rate [23]. ...
This article aims at addressing the future challenges in Sustainability and Information Technology (IT) by reversing the order of the conventional prioritization of social objectives and technology, and placing the aim first and the means second. In engineering and technology, historically, there has been greater focus on first developing the technologies (means) and then determining their potential (aim), and how to tame their unintended consequences. The greatest challenge confronting humanity in the coming decades is sustainability. Therefore, the question is how can IT design, develop, and assist in maintaining the ambitious, albeit difficult to grasp, sustainability agenda? This discussion is pertinent in order to avoid research programs and academic curriculum which dive into the intricacies of IT without viewing sustainability as a core value, which ultimately risks replicating the historical pattern that will generate even more unsustainability.
... These designated spaces can safeguard critical habitats and serve as refuges for bioluminescent species, allowing for their population recovery and long-term persistence [174][175][176]. Furthermore, the implementation of sustainable land-use practices, such as responsible forestry, agriculture, and urban planning, can help minimize habitat destruction and promote coexistence between human activities and bioluminescent ecosystems [177]. Public awareness and education campaigns play a crucial role in fostering conservation efforts. ...
Bioluminescence, the emission of light by living organisms, is a captivating and widespread phenomenon with diverse ecological functions. This comprehensive review explores the biodiversity, mechanisms, ecological roles, and conservation challenges of bioluminescent organisms in Brazil, a country known for its vast and diverse ecosystems. From the enchanting glow of fireflies and glow-in-the-dark mushrooms to the mesmerizing displays of marine dinoflagellates and cnidarians, Brazil showcases a remarkable array of bioluminescent species. Understanding the biochemical mechanisms and enzymes involved in bioluminescence enhances our knowledge of their evolutionary adaptations and ecological functions. However, habitat loss, climate change, and photopollution pose significant threats to these bioluminescent organisms. Conservation measures, interdisciplinary collaborations, and responsible lighting practices are crucial for their survival. Future research should focus on identifying endemic species, studying environmental factors influencing bioluminescence, and developing effective conservation strategies. Through interdisciplinary collaborations, advanced technologies, and increased funding, Brazil can unravel the mysteries of its bioluminescent biodiversity, drive scientific advancements, and ensure the long-term preservation of these captivating organisms.
... W. Wang et al. 2020). The results of this methodology produced a comprehensive land use and land cover change matrix, which is an important analytical tool for extracting relevant information about the nature and spatial patterns of changes in land use (Christensen and Arsanjani 2020). The change matrix functions as a fundamental framework that aids in the identification and measurement of key categories of changes and their corresponding orientations within the designated study region (Ma et al. 2023). ...
The study examines the complex dynamics of changes in LULC over three decades, focused on the years 1992, 2002, 2012, and 2022. The research highlights the significance of comprehending these alterations within the framework of
environmental and socio-economic consequences. The changes in land use and land cover (LULC) have significant and far-reaching effects on ecosystems, biodiversity, and human livelihoods. This study offers useful information for politicians, conservationists, and urban planners by examining historical patterns and forecasting future changes. The study utilized a Multilayer Perceptron Neural Network (MLP-NN), a well-known machine learning technique that excels at collecting intricate patterns. This model’s design had three layers: input,
hidden, and output. The model underwent 10,000 iterations during its training process, and a thorough statistical analysis was conducted to assess the impact of each driving component. The MLP-NN model demonstrated impressive performance, with a skill measure of 0.8724 and an accuracy rate of 89.08%. The accuracy of the LULC estimates for 2022 was verified by comparing them with observed data, ensuring the model’s reliability. Moreover, the presence of evidence
likely was found to be a significant factor that had a substantial impact on the accuracy of the model. The study highlights the effectiveness of the MLP-NN model in accurately predicting changes in LULC. The model’s exceptional accuracy and proficiency make it a powerful tool for future LULC forecasts. Identifying the primary causes of model performance and understanding their implications may help to enhance land management strategies, encourage spatial planning, guide accurate decision-making, and facilitate the development of policies that align with sustainable growth and development.
... As [29] emphasized, in a landscape under heavy human pressure, natural areas are replaced by dominant agricultural activities characterized by fields, fallow land, pastures, and farm buildings. Likewise, [30] indicated that the conversion of forest areas to farmland is a common and increasingly frequent process around Virunga National Park that is resulting in a net loss of natural ecosystems. Agricultural practices in the northwestern Virunga landscape, particularly in the Beni lowlands, have historical roots. ...
... Lacking institutional support, farmers expand fields in the hope of producing more and earning a higher income [16]. Our results also align with those of [30] concerning the significant loss of savannah areas north of Lake Edward, but they contextualize the projection these authors made of an intense and continuous loss of forest cover in the Virunga landscape at a rate of 4.21% by 2030. Forest transition to other land use is discontinuous, as the annual change shows. ...
The Beni region in the eastern Democratic Republic of Congo is grappling with socioeconomic development and security challenges that have affected its natural ecosystems, especially those located in the northern Virunga National Park. This study aims to document the anthropization of the northwestern Virunga landscape from 1995 to 2021 in the context of insecurity. Using a carto-graphic approach and ecological-landscape-analysis tools, this study delves into the overall landscape changes through a comparative analysis of protected and unprotected areas. These investigations focus on landscape composition, transitions between land-cover classes, and the spatial transformation process. The northwestern Virunga landscape is undergoing significant land cover changes due to the influence of insecurity on socioeconomic activities, primarily agriculture. Agricultural land encompasses a larger area than other land-cover types. However, its expansion has decelerated since the 2000s. The loss of forested area is discontinuous. During relatively stable periods (1995-2005), forests exhibited a reduction of up to 2.90% in area, while in the period of the return of Iturian refugees to their province, followed by terrorist insecurity in Beni (2005-2021), the forested area increased by 2.07%. Savannah areas, which are mainly located in the graben rift valley and near Butembo, have been more heavily affected by human activity than forests. Ultimately, the apparent stability of the landscape can be attributed to its protected areas, especially Virunga National Park.
... [6,[19][20][21][22]26,27,31,32,34,37] 3. Concrete Examples, Strategies, and Practical SDGs in Sustainable Urban Development. [25,28,29,[33][34][35][36] ...
... Investigate practical challenges and success factors in implementing SDGs in urban settings. [25,28,29,[33][34][35][36] There is a need for more comprehensive works and studies that link SDGs to specific urban development strategies. ...
Since the definition and publication of the 2030 Agenda in 2015, addressing Sustainable Development Goals (SDGs) has been pivotal in guiding carbon neutrality and sustainable solutions in urban development. Despite the passage of nine years, tangible successes in achieving the SDGs have been limited, underscoring the critical need for innovative approaches to fostering energy performance and reducing carbon emissions. This study advocates for adopting circular economy principles as a strategic pathway to mitigate environmental, social, and economic challenges and promote sustainable, net-zero-energy solutions. Through a systematic literature review spanning multiple databases, this research underscores the synergy between urban circular economies (UCEs) and the SDGs, with a particular focus on sustainable solutions, resource use circularity in construction, and renewable energy integration. By setting stringent eligibility criteria, this review captures a wide array of perspectives, providing a comprehensive analysis that bridges the gap between urban sustainability, renewable energy adoption, and climate change mitigation efforts. The analysis of 23 selected papers reveals a substantial linkage between UCE practices and the advancement of SDGs, highlighting the pivotal roles of responsible consumption, resource efficiency, and regenerative practices in achieving co-benefits through policy and regulatory frameworks towards carbon neutrality. The findings recommend implementing a holistic approach that integrates urban sustainability with circular economy principles, offering a structured insight into the potential of UCEs in fostering a sustainable transition in line with the 2030 Agenda for Sustainable Development.
... This approach is crucial for gaining insights into the transformations and transitions within the land use land cover (LULC) patterns. The understanding gained from a study of these changes and transitions will aid in formulating policies and implementing interventions that align with the Sustainable Development Goals (SDGs) set forth by the United Nations for achieving sustainable development (Christensen and Arsanjani, 2020). ...
Land use land cover change, particularly deforestation has significant implications for global climate and socio-ecological systems as well as resulting ecosystem services from natural systems. In Ghana, the demand for fuel, food, and fibre is projected to be the driver of significant expansion of Croplands/mixed vegetation, resulting in degradation and deforestation of natural ecosystems. This research presents a spatiotemporal analysis of land use/cover change in the Bobiri forest and its surrounding areas in Ghana's moist semi-deciduous forest zone. The study aims to investigate the specific changes in dominant land use land cover (LULC) types in the area using land intensity analyses and to analyse the prevalence of deforestation leakage across the Bobiri Forest Reserve (BFR, a protected area) and its surrounding environs from 1986 and 2022. The study used measured land-cover changes at different levels, including intervals, categories, and transitions. The analysis revealed significant changes in land use intensity across different land classes in the area. The overall rate of land use and land cover change exhibited acceleration, indicating extensive land development throughout the studied periods. Notably, Croplands/mixed vegetation and non-vegetated areas experienced the most gains, while the closed forest class consistently declined. Transitions from forests to Croplands/mixed vegetation were observed, highlighting the conversion of natural vegetation for agricultural purposes. Additionally, the results reveal ongoing leakages in the buffer zone of the BFR as compared to the forest reserve with an annual deforestation rate of (0.64 %) and (0.06 %) respectively from 1986 to 2022, with non-vegetated areas and croplands/mixed vegetation dominating the periphery of protected forest areas. The study recommends implementing policy measures specifically geared towards protecting the buffer zone within a 10 km radius. This is particularly important to the entire buffer zone of the protected area (PA) which is facing deforestation leakage, posing a substantial threat to conservation efforts by exposing the PA to various climatic threats.
... Further, Fig. 9 presents that the literature is concentrated on the use of "artificial intelligence" (Chan and Huang, 2003;Makinde and le Billon, 2022;Sharda et al., 2006;Skiter et al., 2022) in "sustainable natural resource management" (Bettinger et al., 2011;Gupta and Bharat, 2022;Singh and El-Kassar, 2019;Song et al., 2019) using "natural resource modeling" (Davis et al., 1989;Frey, 2021;Rykiel Jr, 1989), "decision support system" (Martell, 2011;Nefeslioglu et al., 2013;Rinaldi et al., 2015;Wikström et al., 2011) for "forest management" (Acosta and Corral, 2017;Newton, 2015) in "ecology" (Skiter et al., 2022), "forestry" (Kauffman and Prisley, 2016;Korosuo et al., 2011), "vegetation mapping" (Adagbasa and Mukwada, 2022) to combat "climate change" (Valero et al., 2022) and facilitate "sustainable development" (Arango-Uribe et al., 2022;Christensen and Arsanjani, 2020;Li et al., 2017;Tanaka et al., 2017). These studies demonstrate AI's potential to support natural resource management, mainly through natural resource modeling, decision support systems, forest management, ecology, and vegetation mapping. ...
... Table 3 provides details of the top 5 authors, their affiliated universities, and the country of publication. Arsanjani is the most relevant author, with an h-index value of 3. The three documents of Arsanjani (Jensen et al., 2020;Christensen & Arsanjani, 2020;Sulova & Arsanjani, 2021) include a comparative study of five different ML techniques to detect invasive plant species using remote sensing data; mapping of future land cover changes through simulation to provide a data-driven decision-making process; and developing an automated and cloud-based workflow, which helps in creating a training dataset for the fire events with the help of openly accessible remote sensing data. Table 4 depicts the country-wise frequency of publications of the ten most relevant countries, along with the citation count and average citations per year. ...
Abstract
This paper reviews the application of AI & ML techniques in achieving the UN Sustainable
Development Goals, as documented in various studies during 2017–2022. A systematic
bibliometric review of a sample of 250 peer-reviewed journal articles selected from two
scientific databases, Scopus and Web of Science, was undertaken (i) to gauge the trend in
publications on the application of specific innovative technologies, especially AI and ML,
for achieving the SDGs; (ii) to analyze the blind spots of AI adversely affecting sustainability,
which are derived from the literature review and to examine the solutions offered in
the literature to counter the adverse effects of AI, and (iii) to gauge the future direction of
research. The highest number of studies originated from China, the USA, Spain, the UK,
and Australia. Evident collaborations between countries and universities are also discernible.
The study identified the journals, Sustainability, Remote Sensing, IEEE Access, and
Journal of Cleaner Production as core sources through Bradford’s law. The findings show
that AI holds promise, but there is overexuberance about its positive outcome. The study
shows a need to impose regulatory requirements and enforce regular verification to ensure
that AI remains a subject of constant scrutiny for trust, transparency, and adherence to universal
ethical standards for SDG achievement. The findings could also provide researchers
with a direction for integrating AI/ML in achieving the SDGs.
Keywords Artificial intelligence (AI) · Bibliometric coupling · Co-citation ·
Co-occurrence · Sustainable development goals (SDG)
... Table 3 provides details of the top 5 authors, their affiliated universities, and the country of publication. Arsanjani is the most relevant author, with an h-index value of 3. The three documents of Arsanjani (Jensen et al., 2020;Christensen & Arsanjani, 2020;Sulova & Arsanjani, 2021) include a comparative study of five different ML techniques to detect invasive plant species using remote sensing data; mapping of future land cover changes through simulation to provide a data-driven decision-making process; and developing an automated and cloud-based workflow, which helps in creating a training dataset for the fire events with the help of openly accessible remote sensing data. Table 4 depicts the country-wise frequency of publications of the ten most relevant countries, along with the citation count and average citations per year. ...
This paper reviews the application of AI & ML techniques in achieving the UN Sustainable Development Goals, as documented in various studies during 2017–2022. A systematic bibliometric review of a sample of 250 peer-reviewed journal articles selected from two scientific databases, Scopus and Web of Science, was undertaken (i) to gauge the trend in publications on the application of specific innovative technologies, especially AI and ML, for achieving the SDGs; (ii) to analyze the blind spots of AI adversely affecting sustainability, which are derived from the literature review and to examine the solutions offered in the literature to counter the adverse effects of AI, and (iii) to gauge the future direction of research. The highest number of studies originated from China, the USA, Spain, the UK, and Australia. Evident collaborations between countries and universities are also discernible. The study identified the journals, Sustainability, Remote Sensing, IEEE Access, and Journal of Cleaner Production as core sources through Bradford’s law. The findings show that AI holds promise, but there is overexuberance about its positive outcome. The study shows a need to impose regulatory requirements and enforce regular verification to ensure that AI remains a subject of constant scrutiny for trust, transparency, and adherence to universal ethical standards for SDG achievement. The findings could also provide researchers with a direction for integrating AI/ML in achieving the SDGs.
... The accuracy of prediction results is evaluated based on comparing each pair of pixels, expressed by the Kappa statistical index system, including Kappa for locationStrata (KlocationStrata), location (Klocation), no information (Kno), and Kappa standard (Kstandard) (Pontius, 2002;Pontius, 2000). The Kappa values for these variants range from 0 to 1 (0% and 100%); The closer to 100% the value reaches, the higher the agreement accuracy (Christensen and Arsanjani, 2020). Overall, there was a significant level of agreement between the prediction and actual LULC maps ( These values are accepted when they are related to the reliability of the model validation for further use (Pontius and Millones, 2011). ...
The lack of ability to control human activities led to changes of land use/land cover (LULC) in Dalat City where rapid urbanization and the demand to expand agricultural land have resulted in dramatic forest reductions. This study assessed the rate and extent of LULC changes over the past 12 years and simulated future scenarios in Dalat City, Lam Dong Province, Vietnam by using an integrated model of Markov chain and logistics regression. Three land-use maps used to analyze land-use change were extracted from satellite images in 2010, 2016, and 2022 by classification approach. The outcome of this process indicates a significant increase in agricultural and built-up land of 48.22 km 2 and 9.36 km 2 , respectively; a decrease in forest land of 55.61 km 2 , and a minor change in water bodies and bare land in the 2010-2022 period. Prediction maps of land-use change in 2028 and 2034 are generated after the model is validated by comparing the actual map with the prediction map of LULC in 2022 using Kappa statistics. Transition of forest area to other land use types, especially land for expansion of built-up and agricultural land is the crucial trend of land-use change in the future according to the forecast model. Compared to 2022, forest area in 2034 will decrease by 60.65 km 2 while built-up and agricultural land will increase by 14.07 km 2 and 43.61 km 2 , respectively. The research results provide valuable information as a foundation for land-use policy planning and local urban development to ensure sustainable development objectives.
