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This study explores the implications of shifting the narrative of climate policy evaluation from one of costs/benefits or economic growth to a message of improving social welfare. Focusing on the costs of mitigation and the associated impacts on gross domestic product (GDP) may translate into a widespread concern that a climate agreement will be very costly. This article considers the well-known Human Development Index (HDI) as an alternative criterion for judging the welfare effects of climate policy. We estimate what the maximum possible annual average increase in HDI welfare per tons of CO2would be within the carbon budget associated with limiting warming to 2°C over the period 2015–2050. Emission pathways are determined by a policy that allows the HDI of poor countries and their emissions to increase under a business-as-usual development path, while countries with a high HDI value (>0.8) have to restrain their emissions to ensure that the global temperature rise does not exceed 2°C. For comparison, the well-known multi-regional RICE model is used to assess GDP growth under the same climate change policy goals. Policy relevance This is the first study that shifts the narrative of climate policy evaluation from one of GDP growth to a message of improving social welfare, as captured by the HDI. This could make it easier for political leaders and climate negotiators to publicly commit themselves to ambitious carbon emission reduction goals, such as limiting global warming to 2°C, as in the (non-binding) agreement made at COP 21 in Paris in 2015. We find that if impacts are framed in terms of growth in HDI per t CO2emission per capita instead of in GDP, the HDI of poor countries and their emissions are allowed to increase under a business-as-usual development path, whereas countries with a high HDI (>0.8) must control emissions so that global temperature rise remains within 2°C. Importantly, a climate agreement is more attractive for rich countries under the HDI than the GDP frame. This is good news, as these countries have to make the major contribution to emissions reductions. [ABSTRACT FROM AUTHOR]

Recent studies showed that climate change and socioeconomic trends are expected to increase flood risks in many regions. However, in these studies, human behavior is commonly assumed to be constant, which neglects interaction and feedback loops between human and environmental systems. This neglect of human adaptation leads to a misrepresentation of flood risk. This article presents an agent-based model that incorporates human decision making in flood risk analysis. In particular, household investments in loss-reducing measures are examined under three economic decision models: (1) expected utility theory, which is the traditional economic model of rational agents; (2) prospect theory, which takes account of bounded rationality; and (3) a prospect theory model, which accounts for changing risk perceptions and social interactions through a process of Bayesian updating. We show that neglecting human behavior in flood risk assessment studies can result in a considerable misestimation of future flood risk, which is in our case study an overestimation of a factor two. Furthermore, we show how behavior models can support flood risk analysis under different behavioral assumptions, illustrating the need to include the dynamic adaptive human behavior of, for instance, households, insurers, and governments. The method presented here provides a solid basis for exploring human behavior and the resulting flood risk with respect to low-probability/high-impact risks. [ABSTRACT FROM AUTHOR]

In this study, we couple an integrated flood damage and agent-based model (ABM) with a gravity model of internal migration and a flood risk module (DYNAMO-M) to project household adaptation and migration decisions under increasing coastal flood risk in France. We ground the agent decision rules in a framework of subjective expected utility theory. This method addresses agent's bounded rationality related to risk perception and risk aversion and simulates the impact of push, pull, and mooring factors on migration and adaptation decisions. The agents are parameterized using subnational statistics, and the model is calibrated using a household survey on adaptation uptake. Subsequently, the model simulates household adaptation and migration based on increasing coastal flood damage from 2015 until 2080. A medium population growth scenario is used to simulate future population development, and sea level rise (SLR) is assessed for different climate scenarios. The results indicate that SLR can drive migration exceeding 8000 and 10,000 coastal inhabitants for 2080 under the Representative Concentration Pathways 4.5 and 8.5, respectively. Although household adaptation to flood risk strongly impacts projected annual flood damage, its impact on migration decisions is small and falls within the 90% confidence interval of model runs. Projections of coastal migration under SLR are most sensitive to migration costs and coastal flood protection standards, highlighting the need for better characterization of both in modeling exercises. The modeling framework demonstrated in this study can be upscaled to the global scale and function as a platform for a more integrated assessment of SLR-induced migration. [ABSTRACT FROM AUTHOR]

Purpose: Over the last few decades, a global increase in both cold and heat extremes has been observed with significant impacts on human mortality. Although it is well-identified that older individuals (> 65 years) are most prone to temperature-related mortality, there is no consensus on the effect of sex. The current study investigated if sex differences in temperature-related mortality exist in the Netherlands. Methods: Twenty-three-year ambient temperature data of the Netherlands were combined with daily mortality data which were subdivided into sex and three age classes (< 65 years, 65–80 years, ≥ 80 years). Distributed lag non-linear models were used to analyze the effect of ambient temperature on mortality and determine sex differences in mortality attributable to the cold and heat, which is defined as mean daily temperatures below and above the Minimum Mortality Temperature, respectively. Results: Attributable fractions in the heat were higher in females, especially in the oldest group under extreme heat (≥ 97.5th percentile), whilst no sex differences were found in the cold. Cold- and heat-related mortality was most prominent in the oldest age group (≥ 80 years) and to a smaller extent in the age group between 65–80 years. In the age group < 65 years temperature-related mortality was only significant for males in the heat. Conclusion: Mortality in the Netherlands represents the typical V- or hockey-stick shaped curve with a higher daily mortality in the cold and heat than at milder temperatures in both males and females, especially in the age group ≥ 80 years. Heat-related mortality was higher in females than in males, especially in the oldest age group (≥ 80 years) under extreme heat, whilst in the cold no sex differences were found. The underlying cause may be of physiological or behavioral nature, but more research is necessary. [ABSTRACT FROM AUTHOR]