Search

A systematic review of literature on community resilience measurement published between 2005 and 2014 revealed that the profound lack of clarity on risk and resilience is one of the main reasons why confusion about terms such as adaptive capacity, resilience, and vulnerability persists, despite the effort spared to operationalise these concepts. Resilience is measured in isolation in some cases, where a shock is perceived to arise external to the system of interest. Problematically, this contradicts the way in which the climate change and disaster communities perceive risk as manifesting itself endogenously as a function of exposure, hazard, and vulnerability. The common conceptualisation of resilience as predominantly positive is problematic as well when, in reality, many undesirable properties of a system are resilient. Consequently, this paper presents an integrative framework that highlights the interactions between risk drivers and coping, adaptive, and transformative capacities, providing an improved conceptual basis for resilience measurement

Disasters pose a growing threat to sustainable development. Disaster risk management efforts have largely failed to arrest the underlying drivers of growing risk globally: uncontrolled urbanization and proliferation of assets in hazardous areas. Resilience provides an opportunity to confront the social-ecological foundations of risk and development; yet it has been vaguely conceptualized, without offering a concrete approach to operationalization. We propose a conceptualization of disaster resilience centred on wellbeing: ‘The ability of a system, community or society to pursue its social, ecological and economic development objectives, while managing its disaster risk over time in a mutually reinforcing way.’ We present a conceptual framework for understanding the interconnections between disasters and development, and outline how it is being operationalized in practice.

"Greening" economic growth discourses are increasingly replacing the catchword of "sustainable development" within national and international policy circles. The core of the argument is that the growth of modern economies may be sustained or even augmented, while policy intervention simultaneously ensures sustained environmental stewardship and improved social outcomes. But how well are these claims scientifically grounded or supported by economic theory, evidence, and applied modeling? We address this question with reference to the economic modeling of climate mitigation policies. We argue that orthodox economic equilibrium and optimization thinking offers, in effect, little support to and insufficiently meaningful interpretations of green growth claims. There are several exciting strands of new economic thinking that are emerging, offering a more robust and empirically validated understanding of the potential for green growth paths. Nonoptimizing simulation models are arguably in a better position to capture socioeconomic system dynamics and the role of macroeconomic policies for sustainability governance. An important caveat, nevertheless, is that when judged against the evidence, greening growth remains to some extent an oxymoron as to date there has been little evidence of substantial decoupling of GDP from carbon-intensive energy use on a wide scale. Moreover, although the rhetoric on "greening the economy" actively supports the need for policy intervention, the macroeconomic reasoning behind this, remains ambiguous. We finally suggest that, although focusing on the dichotomy between growth and the environment is clearly important, the quality of growth and beyond growth dimensions are at least as important for delivering improved macroeconomic governance for sustainability.

Limited studies have shown that disaster risk management (DRM) can be cost-efficient in a development context. Cost-benefit analysis (CBA) is an evaluation tool to analyse economic efficiency. This research introduces quantitative, stochastic CBA frameworks and applies them in case studies of flood and drought risk reduction in India and Pakistan, while also incorporating projected climate change impacts. DRM interventions are shown to be economically efficient, with integrated approaches more cost-effective and robust than singular interventions. The paper highlights that CBA can be a useful tool if certain issues are considered properly, including: complexities in estimating risk; data dependency of results; negative effects of interventions; and distributional aspects. The design and process of CBA must take into account specific objectives, available information, resources, and the perceptions and needs of stakeholders as transparently as possible. Intervention design and uncertainties should be qualified through dialogue, indicating that process is as important as numerical results.

Major natural disasters in recent years have had high human and economic costs, and triggered record high post-disaster relief from governments and international donors. Given the current economic situation worldwide, selecting the most effective disaster risk reduction (DRR) measures is critical. This is especially the case for low- and middle-income countries, which have suffered disproportionally more economic and human losses from disasters. This article discusses a methodology that makes use of advanced probabilistic catastrophe models to estimate benefits of DRR measures. We apply such newly developed models to generate estimates for hurricane risk on residential structures on the island of St. Lucia, and earthquake risk on residential structures in Istanbul, Turkey, as two illustrative case studies. The costs and economic benefits for selected risk reduction measures are estimated taking account of hazard, exposure, and vulnerability. We conclude by emphasizing the advantages and challenges of catastrophe model-based cot-benefit analyses for DRR in developing countries.