The impact of climate change on migration: a synthesis of recent empirical insights Abstract Concern about the human impact of climate change has led to predictions of how people living in areas vulnerable to drought, flood, and temperature changes will respond to such events. Early studies warned that climate change would lead to dramatic increases in human migration as households became unable to adapt to the impacts of climate change. More recently, empirical studies focused on observed climate events and trends have documented how migration flows vary as a function of both the severity of the event and the ability of the household to migrate, among other factors. In this paper, we provide a systematic review of this literature, based on a conceptual framework in which climate shocks (e.g., drought, floods, or temperature extremes) affect (a) household capability to migrate, by depleting household resources necessary for migration, and (b) household vulnerability in staying, by increasing the risk that a household falls (further) into poverty. In combination, these factors help explain four key patterns seen in the empirical literature: (1) climate-induced migration is not necessarily more prevalent among poorer households; (2) climate-induced migration tends to be more prevalent for long-distance domestic moves than local or international moves; (3) slow-onset climate changes (such as droughts) are more likely to induce increased migration than rapid-onset changes (such as floods); and (4) the severity of climate shocks impacts migration in a nonlinear fashion, with impacts influenced by whether the capability or vulnerability channel dominates.

Evaluation of methods for selecting climate models to simulate future hydrological change Abstract A challenge for climate impact studies is the selection of a limited number of climate model projections among the dozens that are typically available. Here, we examine the impacts of methods for climate model selection on projections of runoff change for five different watersheds across the conterminous USA. The results from an ensemble of 29 global climate models and 29 corresponding hydrological model simulations are compared with the results that would have been obtained by applying six different selection methods to the climate model data and using only the selected models to drive the hydrological model. We evaluate each selection method based on whether the runoff projections produced by the method meet the method’s objective and on whether the results are sensitive to the number of models chosen. The Katsavounidis–Kuo–Zhang (KKZ) method, which is intended to maximize the spread in the projected climate change, was the only method that met both criteria for suitability. Although the KKZ method generally performed well, the results from both it and the other methods varied somewhat unpredictably based on region and number of models chosen. This study shows that the methods and models used in similar top–down studies should be carefully chosen and that the results obtained should be interpreted with caution.

Assessing the macroeconomic impacts of individual behavioral changes on carbon emissions Abstract In the last decade, instigated by the Paris agreement and United Nations Climate Change Conferences (COP22 and COP23), the efforts to limit temperature increase to 1.5 °C above pre-industrial levels are expanding. The required reductions in greenhouse gas emissions imply a massive decarbonization worldwide with much involvement of regions, cities, businesses, and individuals in addition to the commitments at the national levels. Improving end-use efficiency is emphasized in previous IPCC reports (IPCC 2014). Serving as the primary ‘agents of change’ in the transformative process towards green economies, households have a key role in global emission reduction. Individual actions, especially when amplified through social dynamics, shape green energy demand and affect investments in new energy technologies that collectively can curb regional and national emissions. However, most energy-economics models—usually based on equilibrium and optimization assumptions—have a very limited representation of household heterogeneity and treat households as purely rational economic actors. This paper illustrates how computational social science models can complement traditional models by addressing this limitation. We demonstrate the usefulness of behaviorally rich agent-based computational models by simulating various behavioral and climate scenarios for residential electricity demand and compare them with the business as usual (SSP2) scenario. Our results show that residential energy demand is strongly linked to personal and social norms. Empirical evidence from surveys reveals that social norms have an essential role in shaping personal norms. When assessing the cumulative impacts of these behavioral processes, we quantify individual and combined effects of social dynamics and of carbon pricing on individual energy efficiency and on the aggregated regional energy demand and emissions. The intensity of social interactions and learning plays an equally important role for the uptake of green technologies as economic considerations, and therefore in addition to carbon-price policies (top-down approach), implementing policies on education, social and cultural practices can significantly reduce residential carbon emissions.

Beyond participation: when citizen engagement leads to undesirable outcomes for nature-based solutions and climate change adaptation Abstract Scholars and practitioners are increasingly promoting so-called nature-based approaches for urban climate change adaptation. There is widespread consensus that they both support and require transdisciplinary approaches, notably by involving citizens in the change process and finding innovative ways to unite different actors’ efforts and capacities. However, there is little empirical evidence regarding the actual value of citizen involvement to sustainability in this field. Against this background, this paper examines whether (or not) current forms and conditions of citizen involvement help to create a platform to support nature-based solutions and ensure a transformative adaptation process. The results show that under current conditions, citizen engagement often hampers sustainable outcomes. In fact, current structures and mechanisms for mainstreaming nature and climate considerations into sectoral planning are limited and, furthermore, neglect citizen involvement. In addition, there is a blind spot with respect to personal spheres of transformation toward sustainability regarding citizens, civil servants, and decision-makers. Key constraints are power structures and the lack of cognitive/emotional and relational capacities required for improved democratic governance. If we are to tap into the potential of nature-based solutions to increase climate adaptation governance, we need targeted financial and human resources, and greater capacity to overcome current constraints and support all levels and phases of mainstreaming, notably planning, implementation, monitoring, and learning.

Probabilistic assessment and projections of US weather and climate risks and economic damages Abstract Weather and climate extremes cause significant economic damages and fatalities. Over the last few decades, the frequency of these disasters and their economic damages have significantly increased in the USA. The prediction of the future evolution of these damages and their relation to global warming and US economic growth is essential for deciding on cost-efficient mitigation pathways. Here we show using a probabilistic extreme value statistics framework that both the increase in US Gross Domestic Product per capita and global warming are significant covariates in probabilistically modeling the increase in economic damages. We also provide evidence that the Pacific Decadal Oscillation affects the number of fatalities. Using the Intergovernmental Panel on Climate Change scenarios, we estimate the potential future economic risks. We find that by 2060, the extreme risks (as measured by 200-year effective return level) will have increased by 3–5.4 times. The damage costs due to extreme risks are projected to be between 0.1 and 0.7% of US Gross Domestic Product by 2060 and could reach 5–16% by 2100.

Observed trends in daily rainfall variability result in more severe climate change impacts to agriculture Abstract There is increasing evidence that climate change is impacting not just total amounts of precipitation, but its temporal dynamics as well. While previous studies have identified the importance that the temporal distribution of daily rainfall has on crop production, climate models often do not represent these distributions consistently with observed trends. The objectives of this study are to (1) evaluate the relationship between rainfall variability and yields for economically important crops in the upper Southeastern United States and (2) assess the potential impact that incorporating observed trends in rainfall variability has on yield projections under future climate change. This study develops statistical models of historic crop yields for five crops, finding that an explanatory variable related to daily rainfall variability, the wet-day Gini coefficient (GC), has a statistically significant negative relationship with crop yields in all cases. These models are then used to estimate the impacts of climate change using an ensemble of downscaled general circulation model (GCM) projections and scenarios that include a continuation of observed GC trends. Most downscaled GCMs evaluated do not project changes in GC consistent with observed trends, and scenarios that assume a continuation of observed trends result in projected yields that are up to 5.8% lower than those directly based on GCM projections. While additional research is needed in the climate science community to better understand how rainfall variability may change in the future, this should be mirrored in the impacts community so that agricultural impact assessments incorporate these potentially important changes.

Explaining participation in Earth Hour: the identity perspective and the theory of planned behavior Abstract Earth Hour is a globally celebrated environmental campaign that is aimed at converting bystanders into active participants in the combat against climate change. Although it has become a global movement, to date, few studies have investigated the motivations behind people’s participation in Earth Hour. The present study fills this gap by examining Earth Hour participation through the integration of the identity perspective and the theory of planned behavior (TPB). We argue that environmental self-identity and humanity identity motivate people to participate in Earth Hour via the pathways identified in the TPB. We tested our model by conducting a survey in Hong Kong (N = 239). Results from a series of mediation analysis revealed that Earth Hour–specific attitude, subjective norm, perceived behavioral control, and moral norm were positively associated with behavioral intention, which in turn predicted actual participation. Further, we found that environmental self-identity and humanity identity were positively associated with attitude and moral norm, which in turn predicted behavioral intention. These results demonstrate the viability of integrating the identity perspective and the TPB to understand people’s performance of specific pro-environmental behavior, including participation in collective action that aims to convert unconcerned individuals into active participants in environmental endeavors (i.e., conversionary collective action), such as Earth Hour. This integrated model can tell researchers and environmental practitioners not only which behavior-specific factors determine people’s behavior but also how these behavior-specific factors arise in the first place.

Building capacities of women for climate change adaptation: Insights from migrant-sending households in Nepal Abstract Women’s capacities are often constrained due to their roles in their household and society, institutional barriers and social norms. These constraints result in low adaptive capacity of women, which make them more vulnerable to hazards. As more men seek employment opportunities away from home, women are required to acquire new capacities to manage new challenges, including risks from climate change. An action research was conducted to assess impacts of capacity building interventions for women left behind in enhancing adaptive capacity of migrant-sending households in rural areas vulnerable to floods in Nepal. This study finds that capacity-building interventions, which aimed to strengthen autonomous adaptation measures (e.g. precautionary savings and flood preparedness), also positively influenced women to approach formal institutions. Besides, the intervention households were more likely to invest a part of the precautionary savings in flood preparedness measures than control households.

Climate finance and disclosure for institutional investors: why transparency is not enough Abstract The finance sector’s response to pressures around climate change has emphasized disclosure, notably through the recommendations of the Financial Stability Board’s Task Force on Climate-related Financial Disclosures (TCFD). The implicit assumption—that if risks are fully revealed, finance will respond rationally and in ways aligned with the public interest—is rooted in the “efficient market hypothesis” (EMH) applied to the finance sector and its perception of climate policy. For low carbon investment, particular hopes have been placed on the role of institutional investors, given the apparent matching of their assets and liabilities with the long timescales of climate change. We both explain theoretical frameworks (grounded in the “three domains”, namely satisficing, optimizing, and transforming) and use empirical evidence (from a survey of institutional investors), to show that the EMH is unsupported by either theory or evidence: it follows that transparency alone will be an inadequate response. To some extent, transparency can address behavioural biases (first domain characteristics), and improving pricing and market efficiency (second domain); however, the strategic (third domain) limitations of EMH are more serious. We argue that whilst transparency can help, on its own it is a very long way from an adequate response to the challenges of ‘aligning institutional climate finance’.

Using ingroup messengers and ingroup values to promote climate change policy Abstract Responses to climate change are strongly linked to political identity and therefore any efforts to promote climate change policy need to take political identity into account. In the current research, we developed communication strategies, informed by the social identity approach, that promoted climate change policies to Republicans and Democrats. In experiment 1 (N = 879), we presented messages to Republican and Democrat participants about a carbon tax policy that differed in terms of whether the policy was endorsed by members of the Republican or Democrat party, and whether the policy was promoted on the basis of Republican or Democrat values. Experiment 2 (N = 1008) adopted the same design but the focus was on a nuclear energy policy. Across both studies, participants had more positive responses—more favorable attitudes, greater support, and stronger intentions to engage in policy-supportive behavior—when the climate change policy was endorsed by members of their ingroup than the outgroup. In experiment 1, Democrat participants (but not Republican participants) also had more positive attitudes to the carbon tax policy when it was framed in a way that aligned with the values of their ingroup. In experiment 2, Democrat participants again had more positive responses to the nuclear energy policy when it was promoted on the basis of ingroup values, whereas values did not influence Republican participants. These findings demonstrate the importance of considering social identity motivations when communicating about climate change policies.