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The welfare impact of a natural disaster depends on its effect on consumption, not only on the direct asset losses and human losses that are usually estimated and reported after disasters. This paper proposes a framework to assess disaster-related consumption losses, starting from an estimate of the asset losses, and leading to the following findings. First, output losses after a disaster destroys part of the capital stock are better estimated by using the average-not the marginal-productivity of capital. A model that describes capital in the economy as a single homogeneous stock would systematically underestimate disaster output losses, compared with a model that tracks capital in different sectors with limited reallocation options. Second, the net present value of disaster-caused consumption losses decreases when reconstruction is accelerated. With standard parameters, discounted consumption losses are only 10 percent larger than asset losses if reconstruction is completed in one year, compared with 80 percent if reconstruction takes 10 years. Third, for disasters of similar magnitude, consumption losses are expected to be lower where the productivity of capital is higher, such as in capital-scarce developing countries. This mechanism may partly compensate for the many other factors that make poor countries and poor people more vulnerable to disasters.

Capital --- Economic Analysis --- Economic Losses --- Natural Disasters

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The welfare impact of a disaster does not only depend on the physical characteristics of the event or its direct impacts in terms of lost lives and assets. Welfare impacts also depend on the ability of the economy to cope, recover, and reconstruct and therefore to minimize aggregate consumption losses. This ability can be referred to as the macroeconomic resilience to natural disasters. Macroeconomic resilience has two components: instantaneous resilience, which is the ability to limit the magnitude of immediate production losses for a given amount of asset losses, and dynamic resilience, which is the ability to reconstruct and recover. Welfare impacts also depend on micro-economic resilience, which depends on the distribution of losses; on households' vulnerability, such as their pre-disaster income and ability to smooth shocks over time with savings, borrowing, and insurance, and on the social protection system, or the mechanisms for sharing risks across the population. The (economic) welfare disaster risk in a country can be reduced by reducing the exposure or vulnerability of people and assets (reducing asset losses), increasing macroeconomic resilience (reducing aggregate consumption losses for a given level of asset losses), or increasing microeconomic resilience (reducing welfare losses for a given level of aggregate consumption losses). The paper proposes rules of thumb to estimate macroeconomic and microeconomic resilience based on the relevant parameters in the economy. It also provides a toolbox of policies to increase macro- or micro-economic resilience and a list of indicators that can be used to build a resilience indicator.

Climate Change Economics --- Economic Losses --- Economic Theory & Research --- Environment --- Finance and Financial Sector Development --- Insurance & Risk Mitigation --- Labor Policies --- Macroeconomics and Economic Growth --- Natural Disaster --- Natural Disasters --- Resilience --- Social Protections and Labor --- Welfare

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This book focuses on the up-to-date studies on the sustainability with changing climate and extremes. The main contributors discussed the changing climate and extreme events, as well as their impacts on natural and human dimension sustainability, including the incorporated social–ecologic and socioeconomic processes. Special attention is given to four main sections: natural disasters in agriculture; urban/rural ecosystem, tourism, and ecosystem service; extreme climate indices, and newly created dataset for climate change.

Research & information: general --- Environmental economics --- trade conflict --- carbon emissions --- import and export trade --- cooperative emission reduction --- meteorological hazards --- risk assessment --- spatial pattern --- population exposure --- Qinghai-Tibet Plateau --- climate change --- slope geohazards --- new geohazard clusters --- extreme cooling events --- Arctic Oscillation --- winter in China --- atmospheric circulation --- GPP --- CMIP6 --- ESM --- STA --- China --- warm days --- cold days --- warm nights --- cold nights --- hot days --- frost days --- compound drought and heatwave events --- complex network --- event synchronization --- atmospheric circulation patterns --- urban agglomeration --- drought --- heat wave --- flood --- GM (1, 1) --- Arctic --- universal thermal climate index (UTCI) --- spatial-temporal changes --- 1979–2019 --- ecosystem services trade-offs --- land-use change --- soil conservation --- carbon storage --- water yield --- precipitation gradient --- Loess Plateau --- climate hazards --- geospatial analysis --- urban adaptation --- risk management --- snow disaster --- risk zoning --- Heilongjiang Province --- precipitation --- model resolutions --- cold region of China --- spatiotemporal distribution --- spatiotemporal variation --- 1961–2019 --- high-resolution and high-quality precipitation data --- independent and non-independent test --- the 0.01° multi-source fusion precipitation product --- extreme precipitation event --- forest types --- NDVI --- AVHRR GIMMS --- temperature range --- precipitation range --- snow cover --- black carbon concentration --- radiative forcing --- northeast China --- high temperature --- mobile phone data --- impact factor --- Zhuhai City --- WRF model --- projection --- short-lived heatwave event --- long-lived heatwave event --- Yangtze River Basin --- central and western Pacific --- thermocline --- yellowfin tuna --- CPUE --- El Niño --- La Niña --- GAM model --- spring soil moisture --- impact mechanism --- Songnen Plain --- Sanjiang Plain --- maize --- diurnal temperature range --- fresh air index --- natural microclimate comfort index --- fresh air–natural microclimate comfort index --- scenic spots --- Fujian province --- extreme climate indices --- temporal and spatial dynamics --- linear trend --- climate abrupt change --- central China --- peanut drought --- Shandong Province --- natural disaster risk assessment principles --- dry-hot wind disaster --- Shandong province --- natural disaster risk assessment principle --- summer maize --- inter- and mixed cropping --- flowering period --- yield --- potato climatic productivity potential --- Inner Mongolia --- effect --- human mobility --- rainfall --- taxi GPS data --- community --- Zhuhai central areas --- citrus --- quality --- future projection --- state-owned forest farms --- human resource allocation --- industrial structure --- coordination and adaptation --- personal structure --- contiguous poverty-stricken areas --- rainstorms and droughts --- direct economic losses --- disaster-affected population --- drought and flood --- vulnerability --- risk prediction --- agroecosystem --- heatwaves --- population exposure change --- global warming --- 1.5 °C warming scenario --- 2.0 °C warming scenario --- n/a --- 1979-2019 --- 1961-2019 --- El Niño --- La Niña --- fresh air-natural microclimate comfort index