Natural disasters, economic growth and sustainable development in China: an empirical study using provincial panel data

Using a newly developed integrated indicator system with entropy weighting, we analyzed the panel data of 577 recorded disasters in 30 provinces of China from 1985–2011 to identify their links with the subsequent economic growth. Meteorological disasters promote economic growth through human capital instead of physical capital. Geological disasters did not trigger local economic growth from 1999–2011. Generally, natural disasters overall had no significant impact on economic growth from 1985–1998. Thus, human capital reinvestment should be the aim in managing recoveries, and it should be used to regenerate the local economy based on long-term sustainable development.

Natural disaster accounting bias and its equivalence across genetic resource stocks

Two sources of bias arise in conventional loss predictions in the wake of natural disasters. One source of bias stems from neglect of accounting for animal genetic resource loss. A second source of bias stems from failure to identify, in addition to the direct effects of such loss, the indirect effects arising from implications impacting animal-human interactions. We argue that, in some contexts, the magnitude of bias imputed by neglecting animal genetic resource stocks is substantial. We show, in addition, and contrary to popular belief, that the biases attributable to losses in distinct genetic resource stocks are very likely to be the same. We derive the formal equivalence across the distinct resource stocks by deriving an envelope result in a model that forms the mainstay of enquiry in subsistence farming and we validate the theory, empirically, in a World-Society-for-the-Protection-of-Animals application

On the environmental valuation of catastrophe: revisiting mining disasters in the United Kingdom, 1851-1962

In the context of environmental valuation of natural disasters, an important component of the evaluation procedure lies in determining the periodicity of events. This paper explores alternative methodologies for determining such periodicity, illustrating the advantages and the disadvantages of the separate methods and their comparative predictions. The procedures employ Bayesian inference and explore recent advances in computational aspects of mixtures methodology. The procedures are applied to the classic data set of Maguire et al (Biometrika, 1952) which was subsequently updated by Jarrett (Biometrika, 1979) and which comprise the seminal investigations examining the periodicity of mining disasters within the United Kingdom, 1851-1962.

Flooding, resettlement and change in livelihoods: evidence from rural Mozambique

Post-disaster development policies, such as resettlement, can have major impacts on communities. This article concerns how and why people's livelihoods change as a result of resettlement, and what relocated people's views of such changes are, in the context of natural disasters. It presents two historically-grounded, comparative case studies of post-flood resettlement in rural Mozambique. The studies show a movement away from rain-fed subsistence agriculture towards commercial agriculture and non-agricultural activities. Ability to secure a viable livelihood was a major determinant of whether resettlers remained in their new locations or returned to the river valleys despite the risks that floods posed. The findings suggest that more research is required into 1) understanding why resettlers choose to stay in or abandon designated resettlement areas; 2) what is meant by 'voluntary' and 'involuntary' resettlement in the context of post-disaster reconstruction; and 3) what the policy drivers for resettlement are in developing countries.

Implications of climate change for expanding cities worldwide

This paper analyses the trends of the changing environmental effects within growing megacities as their diameters exceed 50–100 km and their populations rise beyond 30 million people. The authors consider how these effects are influenced by climate change, to which urban areas themselves contribute, caused by their increasing greenhouse gas emissions associated with rapidly expanding energy use. Other environmental and social factors are assessed, quantitatively and qualitatively, using detailed modelling of urban mesoscale meteorology, which shows how these factors can lead to large conurbations becoming more vulnerable to climatic and environmental hazards. The paper discusses the likely changes in meteorological and hydrological hazards in urban areas, both as the climate changes and the sizes of urban areas grow. Examples are given of how these risks are being reduced through innovations in warning and response systems, planning and infrastructure design, which should include refuges against extreme natural disasters. Policies are shown to be more effective when they are integrated and based on substantial community involvement. Some conclusions are drawn regarding how policies for the natural and artificial environment and for reducing many kinds of climate and hazard risk are related to future designs and planning of infrastructure and open spaces.

Continental and global scale flood forecasting systems

Floods are the most frequent of natural disasters, affecting millions of people across the globe every year. The anticipation and forecasting of floods at the global scale is crucial to preparing for severe events and providing early awareness where local flood models and warning services may not exist. As numerical weather prediction models continue to improve, operational centres are increasingly using the meteorological output from these to drive hydrological models, creating hydrometeorological systems capable of forecasting river flow and flood events at much longer lead times than has previously been possible. Furthermore, developments in, for example, modelling capabilities, data and resources in recent years have made it possible to produce global scale flood forecasting systems. In this paper, the current state of operational large scale flood forecasting is discussed, including probabilistic forecasting of floods using ensemble prediction systems. Six state-of-the-art operational large scale flood forecasting systems are reviewed, describing similarities and differences in their approaches to forecasting floods at the global and continental scale. Currently, operational systems have the capability to produce coarse-scale discharge forecasts in the medium-range and disseminate forecasts and, in some cases, early warning products, in real time across the globe, in support of national forecasting capabilities. With improvements in seasonal weather forecasting, future advances may include more seamless hydrological forecasting at the global scale, alongside a move towards multi-model forecasts and grand ensemble techniques, responding to the requirement of developing multi-hazard early warning systems for disaster risk reduction.