A review of decision-support models for adaptation to climate change in the context of development

Abstract

In order to increase adaptive capacity and empower people to cope with their changing environment, it is imperative to develop decision-support tools that help people understand and respond to challenges and opportunities. Some such tools have emerged in response to social and economic shifts in light of anticipated climatic change. Climate change will play out at the local level, and adaptive behaviours will be influenced by local resources and knowledge. Community-based insights are essential building blocks for effective planning. However, in order to mainstream and scale up adaptation, it is useful to have mechanisms for evaluating the benefits and costs of candidate adaptation strategies. This article reviews relevant literature and presents an argument in favour of using various modelling tools directed at these considerations. The authors also provide evidence for the balancing of qualitative and quantitative elements in assessments of programme proposals considered for financing through mechanisms that have the potential to scale up effective adaptation, such as the Adaptation Fund under the Kyoto Protocol. The article concludes that it is important that researchers and practitioners maintain flexibility in their analyses, so that they are themselves adaptable, to allow communities to best manage the emerging challenges of climate change and the long-standing challenges of development.

The Dilemmas of Citizen Inclusion in Urban Planning and Governance to Enable a 1.5 °C Climate Change Scenario

“…While communities have intimate knowledge of local environmental changes, they are often less aware of the wider causes and effects of climate change. Hence, community-based initiatives use co-learning approaches in which local and external scientific knowledge about climate change complement each other (Nay, Abkowitz, Chu, Gallagher, & Wright, 2014;Reid & Huq, 2014). For instance, in Indore, India, a city vulnerable to water scarcity during droughts, the municipality-through a 'shared learning dialogue' exercise-has proactively engaged local women's groups and slum-dwellers associations to promote awareness and envision alternative ways of water management (Chu, 2017).…”

“…By employing these tools, policy-makers and other stakeholders are partners in the planning process, collectively analysing and interpreting knowledge and its implications for potential interventions. Such processes can address knowledge deficits by focussing on joint knowledge production, building trust in science, clarifying uncertainties, bridging values, and facilitating co-learning (Karl, Susskind, & Wallace, 2007;Nay et al, 2014).…”

Communities and change in the anthropocene: understanding social-ecological vulnerability and planning adaptations to multiple interacting exposures

“…It is useful for examining public policies or actions when key effects can be easily monetized (Leary 1999;Wegner and Pascual 2011;Kull et al 2013;Mechler and Nabiul Islam 2013;Watkiss et al 2014;Nay et al 2014) Futures, planning and deliberation methods…”

Impact of seasonal forecast use on agricultural income in a system with varying crop costs and returns: an empirically-grounded simulation

“…Game: Since games can provide insight into decision processes (Kühberger et al 2002, Castillo et al 2011, Kang et al 2011, Nay et al 2014, we designed a contextualized, dynamic game to investigate how farmers in the field respond to and interpret weather forecasts within their specific environment. Specifically, the farmers were provided with a randomly selected seasonal forecast (figure 3) and asked to select which crops (if any) they would plant for the season.…”

“…Given the complex and simultaneous interactions among biophysical, social, economic, and perceptual factors in farming communities, a coupled natural and human systems framework is critical to developing a comprehensive understanding of the effectiveness of adaptation strategies (Liu et al 2007, Nay et al 2014. Accordingly, agricultural system models include complex dynamics to account for the various factors that shape farmers' immediate environments and subsequent decisions (Graeub et al 2016, Jain et al 2015.…”