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Climate finance is the study of local and global financing of public and private investment that seeks to support mitigation of and adaptation to climate change. In 2017, the review of financial studies launched a competition among scholars to develop research proposals on the topic with the goal of publishing this special volume. We describe the competition, how the nine projects featured in this volume came to be published, and frame their findings within what we view as a broader climate finance research program.

This paper innovatively proposes the Black Kite Algorithm (BKA), a meta-heuristic optimization algorithm inspired by the migratory and predatory behavior of the black kite. The BKA integrates the Cauchy mutation strategy and the Leader strategy to enhance the global search capability and the convergence speed of the algorithm. This novel combination achieves a good balance between exploring global solutions and utilizing local information. Against the standard test function sets of CEC-2022 and CEC-2017, as well as other complex functions, BKA attained the best performance in 66.7, 72.4 and 77.8% of the cases, respectively. The effectiveness of the algorithm is validated through detailed convergence analysis and statistical comparisons. Moreover, its application in solving five practical engineering design problems demonstrates its practical potential in addressing constrained challenges in the real world and indicates that it has significant competitive strength in comparison with existing optimization techniques. In summary, the BKA has proven its practical value and advantages in solving a variety of complex optimization problems due to its excellent performance. The source code of BKA is publicly available at https://www.mathworks.com/matlabcentral/fileexchange/161401-black-winged-kite-algorithm-bka .

Vision transformers have become popular as a possible substitute to convolutional neural networks (CNNs) for a variety of computer vision applications. These transformers, with their ability to focus on global relationships in images, offer large learning capacity. However, they may suffer from limited generalization as they do not tend to model local correlation in images. Recently, in vision transformers hybridization of both the convolution operation and self-attention mechanism has emerged, to exploit both the local and global image representations. These hybrid vision transformers, also referred to as CNN-Transformer architectures, have demonstrated remarkable results in vision applications. Given the rapidly growing number of hybrid vision transformers, it has become necessary to provide a taxonomy and explanation of these hybrid architectures. This survey presents a taxonomy of the recent vision transformer architectures and more specifically that of the hybrid vision transformers. Additionally, the key features of these architectures such as the attention mechanisms, positional embeddings, multi-scale processing, and convolution are also discussed. In contrast to the previous survey papers that are primarily focused on individual vision transformer architectures or CNNs, this survey uniquely emphasizes the emerging trend of hybrid vision transformers. By showcasing the potential of hybrid vision transformers to deliver exceptional performance across a range of computer vision tasks, this survey sheds light on the future directions of this rapidly evolving architecture.

Effective urban planning for climate-driven risks relies on robust climate projections specific to built landscapes. Such projections are absent because of a near-universal lack of urban representation in global-scale Earth system models. Here, we combine climate modelling and data-driven approaches to provide global multi-model projections of urban climates over the twenty-first century. The results demonstrate the inter-model robustness of specific levels of urban warming over certain regions under climate change. Under a high-emissions scenario, cities in the United States, Middle East, northern Central Asia, northeastern China and inland South America and Africa are estimated to experience substantial warming of more than 4 K—larger than regional warming—by the end of the century, with high inter-model confidence. Our findings highlight the critical need for multi-model global projections of local urban climates for climate-sensitive development and support green infrastructure intervention as an effective means of reducing urban heat stress on large scales.An urban climate model emulator has been used with a multi-model archive to estimate that in a high-emissions scenario, many cities will warm by over 4 K during local summers. Near-global relative humidity decreases highlight the potential for green infrastructure and more efficient urban cooling mechanisms.

Managing forests for climate change mitigation requires action by diverse stakeholders undertaking different activities with overlapping objectives and spatial impacts. To date, several forest carbon monitoring systems have been developed for different regions using various data, methods and assumptions, making it difficult to evaluate mitigation performance consistently across scales. Here, we integrate ground and Earth observation data to map annual forest-related greenhouse gas emissions and removals globally at a spatial resolution of 30 m over the years 2001–2019. We estimate that global forests were a net carbon sink of −7.6 ± 49 GtCO2e yr−1, reflecting a balance between gross carbon removals (−15.6 ± 49 GtCO2e yr−1) and gross emissions from deforestation and other disturbances (8.1 ± 2.5 GtCO2e yr−1). The geospatial monitoring framework introduced here supports climate policy development by promoting alignment and transparency in setting priorities and tracking collective progress towards forest-specific climate mitigation goals with both local detail and global consistency.Forest management for climate mitigation plans requires accurate data on carbon fluxes to monitor policy impacts. Between 2001 and 2019, forests were a net sink of carbon globally, although emissions from disturbances highlight the need to reduce deforestation in tropical countries.

The 2002 ‘glonacal’ paper described higher education as a multi-scalar sector where individual and institutional agents have open possibilities and causation flows from any of the interacting local, national and global scales. None have permanent primacy: global activity is growing; the nation-state is crucial in policy, regulation and funding; and like the other scales, the local scale in higher education and knowledge is continually being remade and newly invented. The glonacal paper has been widely used in higher education studies, though single-scale nation-bound methods still have a strong hold. Drawing on insights from human geography and selected empirical studies, the present paper builds on the glonacal paper in a larger theorization of space and scale. It describes how material elements, imagination and social practices interact in making space, which is the sphere of social relations; it discusses multiplicity in higher education space and sameness/different tensions; and it takes further the investigation of one kind of constructed space in higher education, its heterogenous scales (national, local, regional, global etc.). The paper reviews the intersections between scales, especially between national and global, the ever-changing ordering of scales, and how agents in higher education mix and match scales. It also critiques ideas of fixed scalar primacy such as methodological nationalism and methodological globalism—influential in studies of higher education but radically limiting of what can be imagined and practised. Ideas matter. The single-scale visions and scale-driven universals must be cleared away to bring a fuller geography of higher education to life.

Local and indigenous knowledge is being transformed globally, particularly being eroded when pertaining to ecology. In many parts of the world, rural and indigenous communities are facing tremendous cultural, economic and environmental changes, which contribute to weaken their local knowledge base. In the face of profound and ongoing environmental changes, both cultural and biological diversity are likely to be severely impacted as well as local resilience capacities from this loss. In this global literature review, we analyse the drivers of various types of local and indigenous ecological knowledge transformation and assess the directionality of the reported change. Results of this analysis show a global impoverishment of local and indigenous knowledge with 77% of papers reporting the loss of knowledge driven by globalization, modernization, and market integration. The recording of this loss, however, is not symmetrical, with losses being recorded more strongly in medicinal and ethnobotanical knowledge. Persistence of knowledge (15% of the studies) occurred in studies where traditional practices were being maintained consiously and where hybrid knowledge was being produced as a resut of certain types of incentives created by economic development. This review provides some insights into local and indigenous ecological knowledge change, its causes and implications, and recommends venues for the development of replicable and comparative research. The larger implication of these results is that because of the interconnection between cultural and biological diversity, the loss of local and indigenous knowledge is likely to critically threaten effective conservation of biodiversity, particularly in community-based conservation local efforts.

Higher education offers the potential to support glonacal (global, national, and local) development. This study presents new empirical and conceptual insights into the ways in which higher education can help to achieve and exceed the outcomes enshrined in the Sustainable Development Goals. Open-ended online surveys were used to learn how academics in Georgia and Kazakhstan view the contributions of universities to addressing self-identified development challenges; and how universities work with the government and the private sector for realising their glonacal development potential. While the study provides ample evidence on the national manifestations of the developmental role of universities, it also shows that limited academic freedom and institutional autonomy impede the full realisation of the potential of higher education. The assumptions underpinning the academics’ views on how higher education can support development are discussed in the light of an innovative framework of essentialist and anti-essentialist approaches. Juxtaposing the national with the global development missions of universities, the paper raises questions on the possibility of delinking higher education from the immediate human capital and modernisation needs of the nation-state and becoming concerned with the global, on promoting freedom to cultivate intellectual curiosity through education and research, and stimulating a more holistic imaginary of the developmental purposes of higher education.

This research presents implications of the global pandemic for local government resiliency in the United States. The authors explore insights from local government officials and managers on the front lines of response and recovery efforts to the biological natural disaster. Findings from the latest nationwide survey of U.S. local governments regarding their preparedness for weather-related natural disasters also inform responses to the current crisis. Results indicate that local governments are innovating and taking strategic actions to fight the virus, even as COVID-19 has exposed social inequities that are exacerbated as the virus spreads. Survey findings of disaster readiness of local governments to weather-related disasters shows that small, resource-poor governments will not be able to respond well and social inequities will grow. Policy strategies at all levels of government must recognize and account for these inequities as threat of this virus subsides, to support stronger, more effective readiness for the next biological catastrophe.

This paper introduces HLOA, a novel metaheuristic optimization algorithm that mathematically mimics crypsis, skin darkening or lightening, blood-squirting, and move-to-escape defense methods. In crypsis behavior, the lizard changes its color by becoming translucent to avoid detection by its predators. The horned lizard can lighten or darken its skin, depending on whether or not it needs to decrease or increase its solar thermal gain. The skin darkening or lightening strategy is modeled by including the stimulating hormone melanophore rate( α -MHS) that influences these skin color changes. Further, the move-to-evasion strategy is also mathematically described. The horned lizard’s shooting blood defense mechanism, described as a projectile motion, is also modeled. These strategies balance exploitation and exploration mechanisms for local and global search over the solution space. HLOA performance is benchmarked with sixty-three optimization problems from the literature, testbench problems provided in IEEE CEC- 2017 “Constrained Real-Parameter Optimization”, analyzed for dimensions 10, 30, 50, and 100, as well as testbench functions from IEEE CEC-06 2019 “100-Digit Challenge”. Moreover, three real-world constraint optimization applications from IEEE CEC2020 and two engineering problems, the multiple gravity assist optimization and the optimal power flow problem, are also studied. Wilcoxon and Friedman statistics tests compare the HLOA algorithm results against ten recent bio-inspired algorithms. Wilcoxon shows that HLOA provides the optimal solution for most testbench functions more effectively than competing algorithms. At the same time, the Friedman statistics test ranks the HLOA first, and the n-dimensional analysis shows that it performs better on the constrained optimization problems for dimensions 50 and 100. The source code is free and available from https://www.mathworks.com/matlabcentral/fileexchange/159658-horned-lizard-optimization-algorithm-hloa .