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Resilient performance is a crucial characteristic of complex socio-technical systems, enabling them to sustain essential functionality during changing or stressful conditions. Resilience Engineering (RE), a sub-field of safety research, focuses on this perspective of resilience. RE emphasises its “cornerstone model”, presenting the REsystem goals of “anticipating, monitoring, responding and learning”. The cornerstone of learning remains fragmented and undertheorized in the existing literature. This paper aims to enrich RE research and its practical implications by developing a nuanced and comprehensive understanding of the role of learning from resilient performance. To achieve this aim, a scoping literature review was conducted to assess how learning is conceptualised in the RE literature and the theoretical foundations on which previous work rests. The main findings show that RE researchers view learning as the process of understanding the system, sharing knowledge, and re-designing system properties. The application of established learning theories is limited. This paper contributes to research by proposing an organisational process for the RE cornerstone of learning, paving the way for deeper discussions in future studies about learning from resilient performance within complex sociotechnical systems.

The gigantic volumes of carbon dioxide (CO2) removal likely needed to comply with the Paris Agreement beg the question of who should pay for the negative emissions. Incentivizing negative emissions is difficult, as it entails reversing the fiscal attractiveness associated with carbon taxes and emissions trading in favour of the more unattractive need to pay for removals. The inherent difficulty of funding global public goods associated with large private costs will make it hard for future governments to share this burden among themselves. We propose that this problem can be solved by a CO2 emitter liability operationalized through Atmospheric CO2 Removal Deposits (ACORDs). Anyone that emits fossil CO2 to the atmosphere would be obliged to finance the removal of at least as much CO2 from the atmosphere. Linking the liability to ACORDs acknowledges that a major part of the negative emissions needs to be made in the future. The emitters' financial deposits, including earnings, can be redeemed upon certified proof of removal. The ACORDs system would comply with the widely accepted principle of producer liability, i.e., that companies are responsible for the damage caused by their products. The system would also provide additional incentives to reduce emissions and an innovative funding source for coming generations to accomplish negative emissions. Furthermore, inequity and historical emissions can be addressed by gradually increasing overcompensation. The paper also includes a critical assessment of the basis of negative emissions, i.e., the need, the technologies and their potentials, the costs, and the required retention time.

Based upon generalized spillover index method under Markov switching vector auto-regression (MS-VAR) framework, this paper aims at a regime dependent measure of the volatility spillover asymmetry across Shanghai, Hong Kong and the U.S. stock markets. For that, we apply a regime weighted measure of spillover and its asymmetry. The empirical results show that there existed evident negative asymmetric effect of spillover across the three markets, and it was regime dependent and time-varying: it got intensified under high volatility regime and turned out stronger after the Program of Shanghai-Hong Kong stock connect. The regime weighted measure of spillover asymmetry is found to be reliable; it could be explained appropriately with the economic policy uncertainty and investors fear in the U.S., extreme shocks like crash of Shanghai stock market and COVID-19 as well as the capital flow through the Program. From in-sample evidence and out-of-sample forecast of Shanghai stock market, we infer that traditional forecast models could be improved if regime dependent spillover and its asymmetry are considered as additional factors., Funding Agencies|National Social Science Foundation of China [18BJY240]; Overseas Visiting Fellow Program of Shanghai University & the Ministry of Sci- ence and Technology, China [G2023013030]; Overseas Visiting Fellow Program of Shanghai University; Ministry of Science and Technology, China [G2023013030]