Kazunari Tanaka (p)

Carmen Lima

In many urban areas today, evacuation plans have been formulated to cope with disasters such as earthquakes, tsunamis, and the resulting fires. These are express in hazard maps. In the evacuation hazard map, citizens are encouraged to conduct evacuation exercises in each area. Citizens can cope with disasters by conducting such exercises on a continuous basis. However, there are many people who do not participate in evacuation drills, such as the elderly. Many citizens understand the map but do not actually simulate it. When a disaster occurs, many citizens may be hesitant to evacuate to the evacuation sites set in the map. They may be more willing to evacuate to an evacuation site along a path that they often walk, even if it is a bit far away, than to an evacuation site that is closer but unfamiliar to them. The same is true for neighborhood community groups. For them, a distant but well-known town is more likely to be an evacuation site than a nearby neighborhood. In fact, evacuation hazard maps need to be developed based on an understanding of the cognitive structure of such cities.
In a series of studies, we have so far identified the structure and distortion of cognitive maps. In this report, I present the results of a study of methods for creating hazard maps. The methods examined are based on GIS. Three methods were used: one based on urban spatial components, one based on distances, and one based on areal analysis. The advantages and challenges of the different methods are illustrated for the target area.

M. Carmen Lima (p), Ferran Sancho

Lamia Enab

This paper focuses on a contribution to improve multisectoral national/regional analysis. The tools currently used to identify and measure forward effects in disaggregated multi-sector models are widely considered inadequate. While the multi-sector approach is broadly accepted, the Ghosh classical model (1958), commonly employed in these analyses, has faced substantial criticism for two primary reasons. First, the model fails to accurately represent the technological characteristics of a market economy. Second, it gives rise to conceptual inconsistencies in the relationship between value-added and output levels. To overcome these limitations, we propose a new and straightforward method, grounded in standard theory, for identifying and measuring forward effects. Although this approach departs from the Ghosh model, it retains the core principle of examining the dependence of output on value-added, while enabling wider insights for economic analysis and policy evaluation.

Lamia Enab (p)

Peter Meister-Broekema

Solid Waste Management (SWM) has emerged as a critical concern for both developed and developing countries, contributing significantly to environmental pollution and health hazards due to improper handling and disposal practices. The rapid urbanization, population growth, and technological advancements have exacerbated the problem, with global waste generation projected to escalate from 2.01 billion tons in 2018 to 3.40 billion tons by 2050. This research addresses the urgent need for innovative and sustainable Solid Waste Management strategies by exploring the application of Artificial Intelligence (AI) technologies in the developing countries, taking Jordan as a case study, particularly in the urban context of Al-Radwan neighborhood in Amman. AI's potential to revolutionize Solid Waste Management through improved operational efficiency, cost savings, enhanced recycling rates, and data-driven decision-making is examined. The study identifies key research gaps in the perception and empirical evidence of Artificial Intelligence applications in Solid Waste Management. These applications include smart bin level estimation through sensor-based waste monitoring, smart bin systems and waste sorting, and collection improvement: waste collection, vehicle routing and route optimization. In addition, the research explains how Artificial Intelligence can help monitor and track waste throughout the recycling process, optimize the logistics and transportation of recycled waste, and thus explore the feasibility of adopting recycling practices management in the future. Using both secondary data and primary data, the research evaluates AI's role in waste generation, sorting, and collection processes. The findings aim to enhance understanding of AI's benefits and challenges, providing insights for policymakers, municipalities, and SWM organizations to optimize resource efficiency, reduce costs, and transition towards a circular economy. An AI simulation model was built from scratch for the purposes of Al-Radwan neighborhood, to determine the expected results of applying AI in its waste collection improvements. The results are anticipated to contribute significantly to urban planning and sustainable development in Jordan, offering best practices and recommendations that open doors and pave the way for future research studies regarding improving urban planning using AI applications, and be considered as a reference study for other developing countries with similar circumstances.

Peter Meister-Broekema (p)

Gunther Maier

Regional development has often been described in economic terms, using economic indicators such as growth in GDP or demographic indicators such as net migration or employment. Some researchers argued that regional development should be understood broader, by including for example social indicators and living environment indicators. Recently, researchers have shown that policies directed towards regional development have broadened as well, but are also still evaluated within specific narratives or frameworks that often constitute the goals of the policy. This self-constituting practice of an amalgam of related policies has also been referred to as a policy paradigm. Because policies are often evaluated within these policy paradigms it becomes difficult to decontextualise them, disentangle them and compare policies with each other.
In this paper we propose to use a different, more quantitative and comparative method. By applying the above mentioned work from Andy Pike on numerous data sources from EUROSTAT and OECD, researchers from the PREMIUM_EU project developed a new framework that is measuring Regional Development (dubbed “R”) using economic, social and living environment indicators.

Method
By regarding this “R” (and individual indicators) as an outcome of public policies on the local, regional, national and international level and by analysing regional development policies on different levels from 2010 and onwards we believe it is possible to understand the impact of these policies in a more evidenced based sense, regardless of the above mentioned different types of narratives or frameworks.

Based on the work of Moritz Schütz presented during the ERSA 2024 conference, we developed and employed a webcrawler to automatically download and summarise policies from municipalities, regional and national authorities and analyse the results of this exercise.

Findings/results
The webcrawling and -mining exercise in combination with the new set of indicators will offer a much broader and more comprehensive view of the use and necessity of regional development policies. The findings will be discussed in dedicated policy labs with policymakers and researchers from the respective regions.
Discussion/conclusions
Both the new set of indicators and the analysis of the policies are not only innovative, but will also be viewed as speculative. Although we believe that a direct causal relationship between policies and the regional development will be hard to uncover, we do believe that this research will move the field of policy analysis forward, because it is more focused on evidence-based indicators and is based on larger sets of policies.

Gunther Maier (p), Sabine Sedlacek (p)

Kazunari Tanaka

Since John P.A. Ioannidis published his article “Why Most Published Research Findings are False” (Ioannidis, 2005), substantial evidence has accumulated that science and research in their current form have a problem with replicability and reproducibility of their published findings. The discussion initially focused on psychology and medicine, but in the meantime, we know that other natural and social science disciplines are affected as well (Baker, 2016). This issue goes to the heart of science and research, as it undermines the credibility of theoretical concepts that build on those findings and may call into question substantial parts of accumulated scientific knowledge. Most alarmingly, these are not random anomalies, but are related to basic elements of our traditional research paradigm.
To our knowledge, these issues have thus far been largely ignored in Regional Science. We basically do not know whether there is a “reproducibility crisis” in Regional Science as well and if so, how large it is. With our paper we want to take a first step toward answering this question. The paper will examine the publishing behavior of researchers in regional science journals and aim at identifying whether and how researchers provide access to their research designs, data and results. This can be seen as a prerequisite for reproducible research. Only when authors fully disclose these elements, one can even try to replicate their research. In this article, we will concentrate on access to the data. We select articles published in major main regional science outlets (incl. Regional Studies, Papers in Regional Science, Region, Regional Science Policy & Practice) within the period 2022-24. For obvious reasons, we concentrate on empirical papers and exclude purely theoretical and conceptual contributions. By use of data-mining methods, we identify all text areas that contain the string “data”. Then, we read through these text areas and categorize the article by clarity of the provided information about data sources, availability of those data and the level of difficulty by which other researchers can access those data. In the end, we hope to be able to reach a lower bound for the percentage of non-reproducible publications in Regional Science.