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The following article is targeted at both trainers and managers in educational organisations. It aims to provide support to those who would like to implement e-learning and new teaching methods in their organisations and who have a) already experienced resistance to such a change management process, or b)expect potential resistance. The article intends to help this target group by providing an understanding of the role of new media in the process of changing a “learning culture” in an educational organisation. Furthermore it introduces an approach to change management in educational settings which attempts to change resistance from a hindering to a productive element of change processes.
Abstract
Forest-based carbon credits are crucial in most Emissions Trading Schemes as they offer a cost-efficient means of offsetting hard-to-abate emissions. To date, this has not been the case in the European Union Emissions Trading Scheme (EU ETS). However with the Paris Agreement rulebook now finalized, there could be an opportunity to revive this flexibility mechanism in European climate policy. Based on 24 expert interviews, we examined the forest potential within the EU ETS across short, medium, and long-term time frames. We found that the compliance system will remain blocked until 2030, but there is a greater likelihood of transitioning towards the inclusion of forest-based removals and reductions in the long term. Although forestry projects have faced significant reluctance in the EU, there is unanimous agreement on the importance of both technological solutions and such initiatives for climate protection. To fully leverage the potential of forest activity in the future, it will be necessary to adopt different methods and tools (e.g., liability regimes), stricter legislation on socio-economic factors (e.g., land use rights), overcoming implementation hurdles (e.g., do not compromise deterrence through mitigation), and maintaining an open political stance. This study provides a comprehensive perspective on the barriers and potentials of forestry projects within the compliance system of the EU which is essential to be addressed when re-opening the discussion on future eligibility. The implication of the findings suggest an immediate start to adopt to the barriers for carbon credit readiness in the next phase of the EU ETS beginning of 2030.
Abstract
Over the course of the European Sovereign Debt Crisis members of the euro area have put up significant resources to stabilize the financial situation of a few fellow member states. In Germany, this support is subject to a controversial discussion. One aspect in that is the extent of support provided. Using the financial assistance provided to Greece as an example, this paper sheds some light on the financial burden for Germany in comparison to other member states of the euro area, especially Estonia, Latvia and Lithuania. This implies not only an interesting comparison of strains between large and small economies but also between original and later euro area members.
Keywords: euro area, debt crisis, exposure, Greece, Baltic states, Germany
Climate change is a global challenge, with estimated mitigation costs ranging from $1.6 to $3.8 trillion per year. As a pioneer in climate action, the European Union has the most exten-sive emissions trading system worldwide (90% of the global value of $759 billion in 2021). In this paper, we review the European Union's climate strategy, emphasizing the EU Emissions Trading System (EU ETS) development, and the role of tropical forest carbon credits for off-setting. We argue that the European Union continues to leave a significant potential of trop-ical forests as natural carbon sinks unattended. In contrast, we reveal that the regulators can learn from the experiences made in the past and the finalization of the rulebook for Article 6 of the Paris Agreement. We present a proposal on changes to the EU ETS regulation by con-verting the European Commission's proposal to increase the linear reduction factor from 2.2% to 4.2% to the eligibility of forest carbon credits, resulting in additional funding poten-tial for forestry projects to increase necessary carbon sinks. Simultaneously, allowing flexibil-ity of investing to a limited extent in neutralization projects mitigates the risk of overstress-ing regulated companies to reach the emission reduction targets.
Based on a first video conference: small and middle sized companies in Russia and Germany: A comparative view organized by University "Interregional Institute of Economics and Law", Saint Petersburg/Russia and the University of Applied Sciences – Faculty of Business, Fulda/Germany held on 20th May 2010. Both institutes decided to organize a follow-up conference on January 27th 2011. Again the focus was to compare both markets for international operating companies in reasons for going abroad. The following papers are the outcome of this conference and were presented on the one hand by Fulda master students and on the other hand by Master Students from Saint Petersburg. The overall focus was again a comparative work from a company point of view. Hereby the main research question was to present different case studies based on a heterogeneous group of German-based companies in terms of size and branches. Success and failure in international management activities are discussed on an empirical and statistical basis. Furthermore the students from both institutes learnt also some practical matters like for example how can a foreign company establish its legal presence in Russia?
The 1st Jordanian Conference on Logistics in the Mashreq Region was organized within the framework of the research project “JOINOLOG”, funded by the German Ministry of Education and Science.
The project’s conclusion and introduction to an audience of peers was the 1st Jordanian Conference on Logistics in the Mashreq Region (JCLM1), conducted on November 14th and 15th, 2023. These are the proceedings of this final event. They consolidate the shared efforts of all participants and speakers in JCLM1. The collection of these scientific results aims to promote logistical sciences and its transfer into application, which is reflected by their multidimensional presentation in this document.
We explore whether the integration of carbon offsets into investment portfolios improves perfor-mance. Our results show that investment strategies that include such offsets achieve higher Sharpe Ratios than the diversified benchmark portfolios. The efficient frontier of optimal portfolio choices is shifted upwards as a result of including compliance and voluntary carbon offsets in the portfolio. Our results also show that while diversified portfolios may benefit from carbon offsets integration, voluntary carbon offsets are significantly more sensitive to exogenous shocks than compliance carbon allowances. All these results are novel and may encourage investors to invest in such sustainable asset classes.
This article guides you through the development of a successful moderated and collaborative e-learning course on the basis of an e-learning pattern template. The created patterns are a blueprint of the learning activity which could be implemented by using different web-based communication tools. The “e-learning pattern template” takes the special context of online-courses (compared to face-to-face teaching) into account, with a development focus on the participants’ motivation.
Currently, process control in automation technology is mostly regulated by fixed process parameters as a compromise between several identically constructed systems or by plant operators, who are often guided by intuition based on decades of experience. Some operators are not able to pass on their knowledge to the next generation due to societal developments, e.g. academization or increased desire for self-actualization. In contrast, the vision of Smart Factories includes intelligent machining processes that should ultimately lead to self-optimization and adaptation to uncontrollable variables. To consistently implement this vision of self-optimizing machines, a defined quality criterion must be automatically monitored and act as a feedback for continual, autonomous and safe optimization. The term safe refers to the compliance with process quality standards, which must always be maintained. In a very conservative branch such as automation technology, no risks whatsoever are allowed through random experiments for data generation in production operations, since, for example, an unscheduled downtime leads to serious financial losses. Furthermore, machine-driven decisions may at no time pose a threat. Thus, decisions under uncertainty may only be taken where the amount of uncertainty can be considered uncritical. Additionally, industrial applications require a guaranteed real-time capability in terms of reaction to ensure that the actions can be taken in time whenever needed. Since economic aspects are often crucial for decisions in industry, necessary experiments under laboratory conditions, for example, should also be as avoidable as possible, while the effort required for integration into a field application should be as simple as possible.
The aim of this work is the scientific investigation of the integration of learning feedback
for intelligent decision making in the control of industrial processes. The successful integration enables data-driven process optimization. To get closer to the vision of self-optimizing machines, safe optimization methods for industrial applications on the process level are investigated and developed. Here, considering the given restrictions of the automation industry is critical. This work addresses several fields including technical, algorithmic and conceptual aspects. The algorithmic refinements are essential for enabling a wider use of safe optimization for industrial applications. They allow, e.g., the automatic handling of the majority of hyper-parameters and the solution of complex problems by increased computational efficiency. Furthermore, the trade-off between exploration and exploitation of safe optimization in high-dimensional spaces is improved. To account for changeable states perceived via sensor data, contextual Bayesian optimization is modified so that safety requirements are met and real-time capability is satisfied. A software application for industrial safe optimization is implemented within a real-time capable control to be able to interact with other software modules to reach an intelligent decision. Further contributions cover recommendations regarding technical requirements with focus on edge control devices and the conceptual inclusion of machine learning to industrial process control.
To emphasize the application relevance and feasibility of the presented concepts, real world lighthouse projects are realized in the course of this work, indented to reduce skepticism and thus initiate the breakthrough of self-optimizing machines.