How effective are community-based disaster reduction strategies? Evidence from the largest-scale program so far.

Strategies of community-based disaster risk reduction have been advocated for more than 2 decades. However, we still lack in-depth quantitative assessments of the effectiveness of such strategies. Our research is based on a national experiment in this domain: the "Comprehensive Disaster Reduction Demonstration Community" project, a governmental program running in China since 2007. Information on more than 11,000 demonstration communities was collected. Combined with the local disaster information and socioeconomic conditions, the spatiotemporal characteristics of these communities over 12 years and their differences in performance by region and income group were analyzed. We performed an attribution analysis for disaster risk reduction effectiveness. This is the first time a series of quantitative evaluation methods have been applied to verify the effectiveness of a large-scale community-based disaster risk reduction project, both from the perspective of demonstrative effects and loss reduction benefits. Here, we find that the project is obviously effective from these two perspectives, and the disaster loss reduction effectiveness illustrates clear regional differences, where the regional economic level and hazard severity act as important drivers. Significant differences of urban-rural and income call for matching fortification measures, and the dynamic management of demonstration community size is required, since the loss reduction benefit converges when the penetration rate of the demonstration community reaches approximately 4% in a province. These and further results provide diverse implications for community-based disaster risk reduction policies and practices.

A Proposal for Integrating Theories of Complexity for Better Understanding Global Systemic Risks.

The global financial crisis of 2008 has shown that the present financial system involves global systemic risks. The dimension of these risks is hard to grasp with the conceptual tools that have been developed to tackle conventional risks like fire or car accidents. While modern societies know quite well how to deal with conventional risks, we have not yet been equally successful at dealing with global systemic risks. For managing this kind of risks, one needs to understand critical features of specific global systems where many human agents interact in ever changing complex networks. Here we apply two specific dimensions of complexity theory for dealing with global systemic risk in an integrated fashion: normal accidents and extended evolution. Both of them have successfully been applied to the analysis of systemic risks. As a paradigmatic example of global systemic risks, we focus on the global financial crisis that began in 2008, and suggest that the future evolution of the financial system could either see a further increase in complexity, or a reversal to a less complex system. We explore and contrast the implications of normal accident theory and extended evolution perspectives and suggest a four-point research strategy informed by complexity theory for better understanding global systemic risks in financial systems.

COVID-19 heralds a new epistemology of science for the public good.

COVID-19 has revealed that science needs to learn how to better deal with the irreducible uncertainty that comes with global systemic risks as well as with the social responsibility of science towards the public good. Further developing the epistemological principles of new theories and experimental practices, alternative investigative pathways and communication, and diverse voices can be an important contribution of history and philosophy of science and of science studies to ongoing transformations of the scientific enterprise.

The European Green Deal - More Than Climate Neutrality.

The European Green Deal aims at climate neutrality for Europe by 2050, implying a significant acceleration of emission reductions. To gain the necessary support, it needs to reduce regional and social inequalities in Europe. We present objectives in terms of jobs, growth and price stability to complement the emission reduction targets and sketch a proof-of-concept investment profile for reaching these goals. Substantial additional annual public investments, of about 1.8% of pre-COVID-19 GDP, are proposed for the next decade. Their allocation includes retrofitting the European building stock, consciously fostering a renewal of the European innovation system as well as complementary measures in the fields of education and health. The scenario outlined in this article is meant as an input to the urgently needed discussion on how the European Green Deal can shift the EU economy to a new development path that realises a carbon-neutral Europe by 2050 while strengthening European cohesion.

Mechanisms of recurrent outbreak of COVID-19: a model-based study.

Recurrent outbreaks of the coronavirus disease 2019 (COVID-19) have occurred in many countries around the world. We developed a twofold framework in this study, which is composed by one novel descriptive model to depict the recurrent global outbreaks of COVID-19 and one dynamic model to understand the intrinsic mechanisms of recurrent outbreaks. We used publicly available data of cumulative infected cases from 1 January 2020 to 2 January 2021 in 30 provinces in China and 43 other countries around the world for model validation and further analyses. These time series data could be well fitted by the new descriptive model. Through this quantitative approach, we discovered two main mechanisms that strongly correlate with the extent of the recurrent outbreak: the sudden increase in cases imported from overseas and the relaxation of local government epidemic prevention policies. The compartmental dynamical model (Susceptible, Exposed, Infectious, Dead and Recovered (SEIDR) Model) could reproduce the obvious recurrent outbreak of the epidemics and showed that both imported infected cases and the relaxation of government policies have a causal effect on the emergence of a new wave of outbreak, along with variations in the temperature index. Meanwhile, recurrent outbreaks affect consumer confidence and have a significant influence on GDP. These results support the necessity of policies such as travel bans, testing of people upon entry, and consistency of government prevention and control policies in avoiding future waves of epidemics and protecting economy.

Impact Factors and Risk Analysis of Tropical Cyclones on a Highway Network.

Coastal areas typically have high social and economic development and are likely to suffer huge losses due to tropical cyclones. These cyclones have a great impact on the transportation network, but there have been a limited number of studies about tropical-cyclone-induced transportation network functional damages, especially in Asia. This study develops an innovative measurement and analytical tool for highway network functional damage and risk in the context of a tropical cyclone, with which we explored the critical spatial characteristics of tropical cyclones with regard to functional damage to a highway network by developing linear regression models to quantify their relationship. Furthermore, we assessed the network's functional risk and calculated the return periods under different damage levels. In our analyses, we consider the real-world highway network of Hainan province, China. Our results illustrate that the most important spatial characteristics were location (in particular, the midlands), travel distance, landfalling status, and origin coordinates. However, the trajectory direction did not obviously affect the results. Our analyses indicate that the highway network of Hainan province may suffer from a 90% functional damage scenario every 4.28 years. These results have critical policy implications for the transport sector in reference to emergency planning and disaster reduction.

A method for computing the fraction of attributable risk related to climate damages.

The recent decision of the U.S. Supreme Court on the regulation of CO2 emissions from new motor vehicles shows the need for a robust methodology to evaluate the fraction of attributable risk from such emissions. The methodology must enable decisionmakers to reach practically relevant conclusions on the basis of expert assessments the decisionmakers see as an expression of research in progress, rather than as knowledge consolidated beyond any reasonable doubt. This article presents such a methodology and demonstrates its use for the Alpine heat wave of 2003. In a Bayesian setting, different expert assessments on temperature trends and volatility can be formalized as probability distributions, with initial weights (priors) attached to them. By Bayesian learning, these weights can be adjusted in the light of data. The fraction of heat wave risk attributable to anthropogenic climate change can then be computed from the posterior distribution. We show that very different priors consistently lead to the result that anthropogenic climate change has contributed more than 90% to the probability of the Alpine summer heat wave in 2003. The present method can be extended to a wide range of applications where conclusions must be drawn from divergent assessments under uncertainty.

Regulating the geological sequestration of CO2.

Governments worldwide should provide incentives for initial large-scale GS projects to help build the knowledge base for a mature, internationally harmonized GS regulatory framework. Health, safety, and environmental risks of these early projects can be managed through modifications of existing regulations in the EU, Australia, Canada, and the U.S. An institutional mechanism, such as the proposed Federal Carbon Sequestration Commission in the U.S., should gather data from these early projects and combine them with factors such as GS industrial organization and climate regime requirements to create an efficient and adaptive regulatory framework suited to large-scale deployment. Mechanisms to structure long-term liability and fund long-term postclosure care must be developed, most likely at the national level, to equitably balance the risks and benefits of this important climate change mitigation technology. We need to do this right. During the initial field experiences, a single major accident, resulting from inadequate regulatory oversight, anywhere in the world, could seriously endanger the future viability of GS. That, in turn, could make it next to impossible to achieve the needed dramatic global reductions in CO2 emissions over the next several decades. We also need to do it quickly. Emissions are going up, the climate is changing, and impacts are growing. The need for safe and effective CO2 capture with deep GS is urgent.