Ship-source greenhouse gas (GHG) emissions could increase by up to 250% from 2012 levels by 2050 owing to increasing global freight volumes. Binding international legal agreements to regulate GHGs, however, are lacking as technical solutions remain expensive and crucial industrial support is absent. In 2003, IMO adopted Resolution A.963 (23) to regulate shipping CO2 emissions via technical, operational, and market-based routes. However, progress has been slow and uncertain; there is no concrete emission reduction target or definitive action plan. Yet, a full-fledged roadmap may not even emerge until 2023. In this policy analysis, we revisit the progress of technical, operational, and market-based routes and the associated controversies. We argue that 1) a performance-based index, though good-intentioned, has loopholes affecting meaningful CO2 emission reductions driven by technical advancements; 2) using slow steaming to cut energy consumption stands out among operational solutions thanks to its immediate and obvious results, but with the already slow speed in practice, this single source has limited emission reduction potential; 3) without a technology-savvy shipping industry, a market-based approach is essentially needed to address the environmental impact. To give shipping a 50:50 chance for contributing fairly and proportionately to keep global warming below 2°C, deep emission reductions should occur soon.
In this policy evaluation report, the results of the first 2 years of the Interreg funded ABCitiEs project are presented. In total 16 entrepreneurship collectives have been studied in 5 partner regions, i.e. Athens, Vilnius, Varazdin-Cakovec, Manchester and Amsterdam. The report contains an analysis of the cases and gives an overview of the most important opportunities and challenges faced by these cases. On the basis of these result, 4 policy directions have been selected in which improvement are considered most successful, i.e. access to funding, intermediaries, monitoring and experimental learning environments. Also, the report presents the action plans that have been formulated on the basis of these policy directions for the cities involved in this project. In the last 2 years of the project, project partners will implement these action plans in their respective cities.
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During the COVID-19 pandemic, the bidirectional relationship between policy and data reliability has been a challenge for researchers of the local municipal health services. Policy decisions on population specific test locations and selective registration of negative test results led to population differences in data quality. This hampered the calculation of reliable population specific infection rates needed to develop proper data driven public health policy. https://doi.org/10.1007/s12508-023-00377-y
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MUSE supports the CIVITAS Community to increase its impact on urban mobility policy making and advance it to a higher level of knowledge, exchange, and sustainability.As the current Coordination and Support Action for the CIVITAS Initiative, MUSE primarily engages in support activities to boost the impact of CIVITAS Community activities on sustainable urban mobility policy. Its main objectives are to:- Act as a destination for knowledge developed by the CIVITAS Community over the past twenty years.- Expand and strengthen relationships between cities and stakeholders at all levels.- Support the enrichment of the wider urban mobility community by providing learning opportunities.Through these goals, the CIVITAS Initiative strives to support the mobility and transport goals of the European Commission, and in turn those in the European Green Deal.Breda University of Applied Sciences is the task leader of Task 7.3: Exploitation of the Mobility Educational Network and Task 7.4: Mobility Powered by Youth Facilitation.
Due to societal developments, like the introduction of the ‘civil society’, policy stimulating longer living at home and the separation of housing and care, the housing situation of older citizens is a relevant and pressing issue for housing-, governance- and care organizations. The current situation of living with care already benefits from technological advancement. The wide application of technology especially in care homes brings the emergence of a new source of information that becomes invaluable in order to understand how the smart urban environment affects the health of older people. The goal of this proposal is to develop an approach for designing smart neighborhoods, in order to assist and engage older adults living there. This approach will be applied to a neighborhood in Aalst-Waalre which will be developed into a living lab. The research will involve: (1) Insight into social-spatial factors underlying a smart neighborhood; (2) Identifying governance and organizational context; (3) Identifying needs and preferences of the (future) inhabitant; (4) Matching needs & preferences to potential socio-techno-spatial solutions. A mixed methods approach fusing quantitative and qualitative methods towards understanding the impacts of smart environment will be investigated. After 12 months, employing several concepts of urban computing, such as pattern recognition and predictive modelling , using the focus groups from the different organizations as well as primary end-users, and exploring how physiological data can be embedded in data-driven strategies for the enhancement of active ageing in this neighborhood will result in design solutions and strategies for a more care-friendly neighborhood.
Developing a framework that integrates Advanced Language Models into the qualitative research process.Qualitative research, vital for understanding complex phenomena, is often limited by labour-intensive data collection, transcription, and analysis processes. This hinders scalability, accessibility, and efficiency in both academic and industry contexts. As a result, insights are often delayed or incomplete, impacting decision-making, policy development, and innovation. The lack of tools to enhance accuracy and reduce human error exacerbates these challenges, particularly for projects requiring large datasets or quick iterations. Addressing these inefficiencies through AI-driven solutions like AIDA can empower researchers, enhance outcomes, and make qualitative research more inclusive, impactful, and efficient.The AIDA project enhances qualitative research by integrating AI technologies to streamline transcription, coding, and analysis processes. This innovation enables researchers to analyse larger datasets with greater efficiency and accuracy, providing faster and more comprehensive insights. By reducing manual effort and human error, AIDA empowers organisations to make informed decisions and implement evidence-based policies more effectively. Its scalability supports diverse societal and industry applications, from healthcare to market research, fostering innovation and addressing complex challenges. Ultimately, AIDA contributes to improving research quality, accessibility, and societal relevance, driving advancements across multiple sectors.
