Hasil untuk "Economic growth, development, planning"

Sijie Yang, Binyu Lei, Filip Biljecki

Ensuring liveability and comfort is one of the fundamental objectives of urban planning. Numerous studies have employed computational methods to assess and quantify factors related to urban comfort such as greenery coverage, thermal comfort, and walkability. However, a clear definition of urban comfort and its comprehensive evaluation framework remain elusive. Our research explores the theoretical interpretations and methodologies for assessing urban comfort within digital planning, emphasising three key dimensions: multidimensional analysis, data support, and AI assistance.

Jakub Growiec, Klaus Prettner

Recent advances in artificial intelligence (AI) have led to a diverse set of predictions about its long-term impact on humanity. A central focus is the potential emergence of transformative AI (TAI), eventually capable of outperforming humans in all economically valuable tasks and fully automating labor. Discussed scenarios range from human extinction after a misaligned TAI takes over ("AI doom") to unprecedented economic growth and abundance ("post-scarcity"). However, the probabilities and implications of these scenarios remain highly uncertain. Here, we organize the various scenarios and evaluate their associated existential risks and economic outcomes in terms of aggregate welfare. Our analysis shows that even low-probability catastrophic outcomes justify large investments in AI safety and alignment research. We find that the optimizing representative individual would rationally allocate substantial resources to mitigate extinction risk; in some cases, she would prefer not to develop TAI at all. This result highlights that current global efforts in AI safety and alignment research are vastly insufficient relative to the scale and urgency of existential risks posed by TAI. Our findings therefore underscore the need for stronger safeguards to balance the potential economic benefits of TAI with the prevention of irreversible harm. Addressing these risks is crucial for steering technological progress toward sustainable human prosperity.

Claudio Lissona, Esther Ruiz

We analyse economic growth vulnerability of the four largest Euro Area (EA) countries under stressed macroeconomic and financial conditions. Vulnerability, measured as a lower quantile of the growth distribution conditional on EA-wide and country-specific underlying factors, is found to be higher in Germany, which is more exposed to EA-wide economic conditions, and in Spain, which has large country-specific sectoral dynamics. We show that, under stress, financial factors amplify adverse macroeconomic conditions. Furthermore, even severe sectoral (financial or macro) shocks, whether common or country-specific, fail to fully explain the vulnerability observed under overall stress. Our results underscore the importance of monitoring both local and EA-wide macro-financial conditions to design effective policies for mitigating growth vulnerability.

Theophilus G. Baidoo, Ashley Obeng

Inflation remains a persistent challenge for many African countries. This research investigates the critical role of machine learning (ML) in understanding and managing inflation in Ghana, emphasizing its significance for the country's economic stability and growth. Utilizing a comprehensive dataset spanning from 2010 to 2022, the study aims to employ advanced ML models, particularly those adept in time series forecasting, to predict future inflation trends. The methodology is designed to provide accurate and reliable inflation forecasts, offering valuable insights for policymakers and advocating for a shift towards data-driven approaches in economic decision-making. This study aims to significantly advance the academic field of economic analysis by applying machine learning (ML) and offering practical guidance for integrating advanced technological tools into economic governance, ultimately demonstrating ML's potential to enhance Ghana's economic resilience and support sustainable development through effective inflation management.

Oliver Niggemann, Gautam Biswas, Alexander Diedrich et al.

The workshop 'AI-based Planning for Cyber-Physical Systems', which took place on February 26, 2024, as part of the 38th Annual AAAI Conference on Artificial Intelligence in Vancouver, Canada, brought together researchers to discuss recent advances in AI planning methods for Cyber-Physical Systems (CPS). CPS pose a major challenge due to their complexity and data-intensive nature, which often exceeds the capabilities of traditional planning algorithms. The workshop highlighted new approaches such as neuro-symbolic architectures, large language models (LLMs), deep reinforcement learning and advances in symbolic planning. These techniques are promising when it comes to managing the complexity of CPS and have potential for real-world applications.

Vladimir Skavysh, Sofia Priazhkina, Diego Guala et al.

Computational methods both open the frontiers of economic analysis and serve as a bottleneck in what can be achieved. We are the first to study whether Quantum Monte Carlo (QMC) algorithm can improve the runtime of economic applications and challenges in doing so. We provide a detailed introduction to quantum computing and especially the QMC algorithm. Then, we illustrate how to formulate and encode into quantum circuits (a) a bank stress testing model with credit shocks and fire sales, (b) a neoclassical investment model solved with deep learning, and (c) a realistic macro model solved with deep neural networks. We discuss potential computational gains of QMC versus classical computing systems and present a few innovations in benchmarking QMC.

Iria Vázquez Silva, Javier de Rivera Outomuro

Este artículo presenta un estudio de caso sobre el vínculo entre la gestión de residuos de plásticos en Marruecos (principalmente en Casablanca y Rabat) y las divergentes condiciones laborales de los trabajadores (formales e informales) que conforman este sector, en el contexto de transformación del sector hacia la Economía Circular (EC). Para ello, tomamos en cuenta la perspectiva de múltiples expertos/as en la materia, así como de la administración pública encargada de gestionar el proceso de transición, el sector empresarial privado y los propios recolectores. Este enfoque nos permitió conocer las complejidades y matices de la transición de la gestión de residuos en Marruecos e identificar los principales retos en materia de justicia social asociados al cambio hacia una Economía Circular.

Mohamed Harmanani

The coronavirus disease 2019 (COVID-19) pandemic has been ongoing for around 3 years, and has infected over 750 million people and caused over 6 million deaths worldwide at the time of writing. Throughout the pandemic, several strategies for controlling the spread of the disease have been debated by healthcare professionals, government authorities, and international bodies. To anticipate the potential impact of the disease, and to simulate the effectiveness of different mitigation strategies, a robust model of disease spread is needed. In this work, we explore a novel approach based on probabilistic planning and dynamic graph analysis to model the spread of COVID-19 in indoor spaces. We endow the planner with means to control the spread of the disease through non-pharmaceutical interventions (NPIs) such as mandating masks and vaccines, and we compare the impact of crowds and capacity limits on the spread of COVID-19 in these settings. We demonstrate that the use of probabilistic planning is effective in predicting the amount of infections that are likely to occur in shared spaces, and that automated planners have the potential to design competent interventions to limit the spread of the disease. Our code is fully open-source and is available at: https://github.com/mharmanani/prob-planning-covid19 .

Theodoros Chatzivasileiadis, Ignasi Cortes Arbues, Jochen Hinkel et al.

This study investigates the long-term economic impact of sea-level rise (SLR) on coastal regions in Europe, focusing on Gross Domestic Product (GDP). Using a novel dataset covering regional SLR and economic growth from 1900 to 2020, we quantify the relationships between SLR and regional GDP per capita across 79 coastal EU & UK regions. Our results reveal that the current SLR has already negatively influenced GDP of coastal regions, leading to a cumulative 4.7% loss at 39 cm of SLR. Over the 120 year period studied, the actualised impact of SLR on the annual growth rate is between -0.02% and 0.04%. Extrapolating these findings to future climate and socio-economic scenarios, we show that in the absence of additional adaptation measures, GDP losses by 2100 could range between -6.3% and -20.8% under the most extreme SLR scenario (SSP5-RCP8.5 High-end Ice, or -4.0% to -14.1% in SSP5-RCP8.5 High Ice). This statistical analysis utilising a century-long dataset, provides an empirical foundation for designing region-specific climate adaptation strategies to mitigate economic damages caused by SLR. Our evidence supports the argument for strategically relocating assets and establishing coastal setback zones when it is economically preferable and socially agreeable, given that protection investments have an economic impact.

Geraud Nangue Tasse, Benjamin Rosman, Steven James

We propose world value functions (WVFs), a type of goal-oriented general value function that represents how to solve not just a given task, but any other goal-reaching task in an agent's environment. This is achieved by equipping an agent with an internal goal space defined as all the world states where it experiences a terminal transition. The agent can then modify the standard task rewards to define its own reward function, which provably drives it to learn how to achieve all reachable internal goals, and the value of doing so in the current task. We demonstrate two key benefits of WVFs in the context of learning and planning. In particular, given a learned WVF, an agent can compute the optimal policy in a new task by simply estimating the task's reward function. Furthermore, we show that WVFs also implicitly encode the transition dynamics of the environment, and so can be used to perform planning. Experimental results show that WVFs can be learned faster than regular value functions, while their ability to infer the environment's dynamics can be used to integrate learning and planning methods to further improve sample efficiency.

Pietro Folco, Laetitia Gauvin, Michele Tizzoni et al.

Developing safe infrastructure for micromobility like bicycles or e-scooters is an efficient pathway towards climate-friendly, sustainable, and livable cities. However, urban micromobility infrastructure is typically planned ad-hoc and at best informed by survey data. Here we study how data of micromobility trips and crashes can shape and automatize such network planning processes. We introduce a parameter that tunes the focus between demand-based and safety-based development, and investigate systematically this tradeoff for the city of Turin. We find that a full focus on demand or safety generates different network extensions in the short term, with an optimal tradeoff in-between. In the long term our framework improves overall network quality independent of short-term focus. Thus, we show how a data-driven process can provide urban planners with automated assistance for variable short-term scenario planning while maintaining the long-term goal of a sustainable, city-spanning micromobility network.

Johannes Jaerisch, Hiroki Takahasi

We perform a multifractal analysis of homological growth rates of oriented geodesics on hyperbolic surfaces. Our main result provides a formula for the Hausdorff dimension of level sets of prescribed growth rates in terms of a generalized Poincaré exponent of the Fuchsian group. We employ symbolic dynamics developed by Bowen and Series, ergodic theory and thermodynamic formalism to prove the analyticity of the dimension spectrum.

Olivier Sykes

This issue (5.2) of Transactions of AESOP brings together a selection of papers which address current themes and issues in planning education. Two of the papers reflect on the experience of teaching modules submitted to recent rounds of the AESOP Excellence in Teaching Award (ETA), one reports on an experience of internationalisation in planning education, and one is an invited paper by Andrea Frank the present Chair of the AESOP ETA Committee. They all provide original and insightful contributions addressing key themes in contemporary planning education including, the impacts of the COVID-19 pandemic, new technologies and modes of teaching delivery, the teaching of landscape in planning programmes, and, the internationalisation of planning cohorts and curricula.

Danila Rijavec, Primož Pevcin

This paper fits into the ex-post migration crisis of 2015-16 dialogue and offers added value through its complex transboundary perspective while bringing in the national perspective of a transboundary crisis. After the largest migration flow, lacking supranational coordination and governance, Slovenia’s coping strategy was oriented towards logistical mechanisms to keep up the pressure and move the flow forward. Given the scale of the crisis, such a setting lacked a rapid response at the local level, and the high dimensionality and nonlinear interactions caused pink noise. Using a case study method, the paper argues that crisis management moved backwards and had a decoupled structure. It also calls for a more inclusive multi-level crisis management structure and investment in existing international organizations. Indeed, if the crisis interactions had taken place globally, the crisis would be less dimensional and more linear, thus avoiding pink noise.

Haojiao Liang, Huiping Li, Jian Gao et al.

Energy efficiency and safety are two critical objectives for marine vehicles operating in environments with obstacles, and they generally conflict with each other. In this paper, we propose a novel online motion planning method of marine vehicles which can make trade-offs between the two design objectives based on the framework of economic model predictive control (EMPC). Firstly, the feasible trajectory with the most safety margin is designed and utilized as tracking reference. Secondly, the EMPC-based receding horizon motion planning algorithm is designed, in which the practical consumed energy and safety measure (i.e., the distance between the planning trajectory and the reference) are considered. Experimental results verify the effectiveness and feasibility of the proposed method.

Aditi Bhatia-Kalluri

This paper addresses the struggle of rural micro-entrepreneurs in the Global South in utilizing e-commerce to reach wider markets. This research paper looks at the adoption of e-commerce as a sustainable marketplace by the micro-entrepreneur sellers from the lower socio-economic rural communities in India, a booming digital economy in the Global South. 'Sustainability' here refers to a model for sustainable economic development sustaining the e-commerce as business model for the rural micro-entrepreneurs to flourish. This paper explores rural development by dismantling the factors that shape the ways technology and trade impact micro-entrepreneurs. The aim is to offer recommendations and solutions to contribute building the e-commerce as a sustainable marketplace for rural micro-entrepreneurs. Recent information and economic policy changes in India, along with the expansion of mobile infrastructure and a growing user base in rural regions makes this research timely and important. By scrutinizing the infrastructure and auditing the information needs and challenges of users, this research will illuminate the gaps that are leading to a lack of sustainable economic development, and information asymmetries discouraging the rural micro-entrepreneurs from selling online. The purpose of the paper is to find hurdles in the sustainable development of e-commerce as a business solution.

E. McKinley, Oscar Aller-Rojas, C. Hattam et al.

Ocean- and coastal-based economic activities are increasingly recognised as key drivers for supporting global economies. This move towards the “blue economy” is becoming globally widespread, with the recognition that if ocean-based activities are to be sustainable, they will need to move beyond solely extractive and exploitative endeavours, aligning more closely with marine conservation and effective marine spatial planning. In this paper we define the “blue economy” as a “platform for strategic, integrated and participatory coastal and ocean development and protection that incorporates a low carbon economy, the ecosystem approach and human well-being through advancing regional industries, services and activities”. In Peru, while the seas contribute greatly to the national economy, the full potential of the blue economy has yet to be realised. This paper presents the findings of an early career scientist workshop in Lima, Peru, in March 2016. The workshop “Advancing Green Growth in Peru” brought together researchers to identify challenges and opportunities for green growth across three Peruvian economic sectors—tourism, transport and the blue economy with this paper exploring in detail the priorities generated from the “blue economy” stream. These priorities include themes such as marine spatial planning, detailed evaluations of existing maritime industries (e.g. guano collection and fisheries), development of an effective MPA network, support for sustainable coastal tourism, and better inclusion of social science disciplines in understanding societal and political support for a Peruvian blue economy. In addition, the paper discusses the research requirements associated with these priorities. While not a comprehensive list, these priorities provide a starting point for future dialogue on a co-ordinated scientific platform supporting the blue growth agenda in Peru, and in other regions working towards a successful “blue economy”.

Małgorzata Wosiek

Research background: The subject of the study is the disproportionate development of rural and urban areas in terms of human capital in the context of the convergence process. Purpose of the article: The main goal of the study is to assess the rural–urban disparities on the educational attainment of the population (adopted as a human capital proxy), based on the example of Poland. Methods: The Bray-Curtis measure of structures diversity, the Kruskal-Wallis test and regression analysis were applied to investigate the scale and dynamic of the rural–urban educational divide in Poland in the period 1988–2018. Findings & Value added: The paper emphasizes the aspect of rural–urban differences in the Polish economy and their dynamic nature. Studies have revealed that in 1988–2018, in Poland, the disparity in educational attainment between rural and urban populations was gradually reduced. This process, however, was not accompanied by the reduction of internal educational disparities in the rural space. The study results are helpful in verifying the effectiveness of public funds, allocated in recent years in order to accelerate the multi-functional development of rural areas in Poland and other CEE countries.

Isabel M. Rayas Fernández, Giovanni Sutanto, Peter Englert et al.

Motion planning with constraints is an important part of many real-world robotic systems. In this work, we study manifold learning methods to learn such constraints from data. We explore two methods for learning implicit constraint manifolds from data: Variational Autoencoders (VAE), and a new method, Equality Constraint Manifold Neural Network (ECoMaNN). With the aim of incorporating learned constraints into a sampling-based motion planning framework, we evaluate the approaches on their ability to learn representations of constraints from various datasets and on the quality of paths produced during planning.

Kiril Solovey

This is a chapter in the Encyclopedia of Robotics. It is devoted to the study of complexity of complete (or exact) algorithms for robot motion planning. The term ``complete'' indicates that an approach is guaranteed to find the correct solution (a motion path or trajectory in our setting), or to report that none exists otherwise (in case that for instance, no feasible path exists). Complexity theory is a fundamental tool in computer science for analyzing the performance of algorithms, in terms of the amount of resources they require. (While complexity can express different quantities such as space and communication effort, our focus in this chapter is on time complexity.) Moreover, complexity theory helps to identify ``hard'' problems which require excessive amount of computation time to solve. In the context of motion planning, complexity theory can come in handy in various ways, some of which are illustrated here.