Adaptive Capacity Index Research Articles

Managing Murray–Darling Basin livestock systems in a variable and changing climate: challenges and opportunities

The key biophysical impacts associated with projected climate change in the Murray–Darling Basin (MDB) include: declines in pasture productivity, reduced forage quality, livestock heat stress, greater problems with some pests and weeds, more frequent droughts, more intense rainfall events, and greater risks of soil degradation. The most arid and least productive rangelands in the MDB region may be the most severely impacted by climate change, while the more productive eastern and northern grazing lands in the MDB may provide some opportunities for slight increases in production. In order to continue to thrive in the future, livestock industries need to anticipate these changes, prepare for uncertainty, and develop adaptation strategies now. While climate change will have direct effects on livestock, the dominant influences on grazing enterprises in the MDB will be through changes in plant growth and the timing, quantity and quality of forage availability. Climate change will involve a complex mix of responses to rising atmospheric carbon dioxide levels, rising temperatures, changes in rainfall and other weather factors, and broader issues related to how people collectively and individually respond to these changes. Enhancing the ability of individuals to respond to a changing climate will occur through building adaptive capacity. We have, via secondary data, selected from the Australian Agricultural and Grazing Industries Survey, built a national composite index of generic adaptive capacity of rural households. This approach expresses adaptive capacity as an emergent property of the diverse forms of human, social, natural, physical and financial capital from which livelihoods are derived. Human capital was rated as ‘high’ across the majority of the MDB compared with the rest of Australia, while social, physical and financial capital were rated as ‘moderate’ to ‘low’. The resultant measure of adaptive capacity, made up of the five capitals, was ‘low’ in the northern and central-west regions of the MDB and higher in the central and eastern parts possibly indicating a greater propensity to adapt to climate change in these regions.

The vulnerability of Australian rural communities to climate variability and change: Part II—Integrating impacts with adaptive capacity

Abstract In the first paper in this series [Nelson, R., Kokic, P., Crimp, S., Martin, P., Meinke, H., Howden, S.M. (2010, this issue)], we concluded that hazard/impact modelling needs to be integrated with holistic measures of adaptive capacity in order to provide policy-relevant insights into the multiple and emergent dimensions of vulnerability. In this paper, we combine hazard/impact modelling with an holistic measure of adaptive capacity to analyse the vulnerability of Australian rural communities to climate variability and change. Bioeconomic modelling was used to model the exposure and sensitivity of Australian rural communities to climate variability and change. Rural livelihoods analysis was used as a conceptual framework to construct a composite index of adaptive capacity using farm survey data. We then show how this integrated measure of vulnerability provides policy-relevant insights into the constraints and options for building adaptive capacity in rural communities. In the process, we show that relying on hazard/impact modelling alone can lead to entirely erroneous conclusions about the vulnerability of rural communities, with potential to significantly misdirect policy intervention. We provide a preliminary assessment of which Australian rural communities are vulnerable to climate variability and change, and reveal a complex set of interacting environmental, economic and social factors contributing to vulnerability.

The determinants of vulnerability and adaptive capacity at the municipal level: Evidence from floodplain management programs in the United States

This article provides a quantitative test of the relationship between adaptive capacity and socio-economic status (SES) at the municipal level. Local participation in a U.S. floodplain management program, the Community Rating System (CRS), is used as an indicator of adaptive capacity. The relationship between aggregated measures of SES and CRS participation is tested using probit, OLS and tobit models. The analysis indicates that socio-economic characteristics of a municipality's population are associated with the capacity of municipal leaders to effect collective action in response to environmental challenges. More theoretical work and case studies are needed to fully explain the reasons for this association.

Development of an adaptive capacity indicator to climate change in the agricultural water sector

Development of an adaptive capacity indicator to climate change in the agricultural water sector

Analysis of farm performance in Europe under different climatic and management conditions to improve understanding of adaptive capacity

The aim of this paper is to improve understanding of the adaptive capacity of European agriculture to climate change. Extensive data on farm characteristics of individual farms from the Farm Accountancy Data Network (FADN) have been combined with climatic and socio-economic data to analyze the influence of climate and management on crop yields and income and to identify factors that determine adaptive capacity. A multilevel analysis was performed to account for regional differences in the studied relationships. Our results suggest that socio-economic conditions and farm characteristics should be considered when analyzing effects of climate conditions on farm yields and income. Next to climate, input intensity, economic size and the type of land use were identified as important factors influencing spatial variability in crop yields and income. Generally, crop yields and income are increasing with farm size and farm intensity. However, effects differed among crops and high crop yields were not always related to high incomes, suggesting that impacts of climate and management differ by impact variable. As farm characteristics influence climate impacts on crop yields and income, they are good indicators of adaptive capacity at farm level and should be considered in impact assessment models. Different farm types with different management strategies will adapt differently.

Uncertainty in adaptive capacity and the importance of scale

Understanding different adaptive capacities is a prerequisite for targeting interventions to reduce the adverse impacts of climate change. Indicators and indices are common tools in this process, but their construction embodies many uncertainties, not least of which is their scale specificity. This paper describes the development of two empirical adaptive capacity indices for use at different scales of analysis: a national index for cross-country comparison in Africa and a household index for cross-household comparison in a village in Limpopo province, South Africa. Explaining the decisions made at each stage of construction illuminates the degree of uncertainty involved when assessing adaptive capacity, and how this uncertainty is compounded when looking across different scales of analysis. It concludes that the central elements of adaptive capacity, based on institutional collective response and the availability of and access to resources, are common at different scales, although the structure of each index is scale-specific. Hence the findings of these apparently irreconcilable scales of analysis converge to demonstrate points of leverage for policy intervention to raise resilience and the capacity to adapt to the risks posed by climate change.

Using expert judgment to assess adaptive capacity to climate change: Evidence from a conjoint choice survey

We use conjoint choice questions to ask a sample of public health and climate change experts contacted at professional meetings in 2003 and 2004 ( n = 100 ) which of two hypothetical countries, A or B, they deem to have the higher adaptive capacity to certain effects of climate change on human health. These hypothetical countries are described by a vector of seven attributes, including per capita income, inequality in the distribution of income, measures of the health status of the population, the health care system, and access to information. Probit models indicate that our respondents regard per capita income, inequality in the distribution of income, universal health care coverage, and high access to information as important determinants of adaptive capacity. A universal-coverage health care system and a high level of access to information are judged to be equivalent to $12,000–$14,000 in per capita income. We use the estimated coefficients and country socio-demographics to construct an index of adaptive capacity for several countries. In panel-data regressions, this index is a good predictor of mortality in climatic disasters, even after controlling for other determinants of sensitivity and exposure, and for per capita income. We conclude that our conjoint choice questions provide a novel and promising approach to eliciting expert judgments in the climate change arena.

Applying an index of adaptive capacity to climate change in north-western Victoria, Australia

Applying an index of adaptive capacity to climate change in north-western Victoria, Australia

Using Expert Judgment to Assess Adaptive Capacity to Climate Change: Evidence from a Conjoint Choice Survey

We use conjoint choice questions to ask public health and climate change experts, contacted at professional meetings in 2003 and 2004, which of two hypothetical countries, A or B, they deem to have the higher adaptive capacity to certain effects of climate change on human health. These hypothetical countries are described by a vector of seven attributes, including per capita income, inequality in the distribution of income, measures of the health status of the population, the health care system, and access to information. Probit models indicate that our respondents regard per capita income, inequality in the distribution of income, universal health care coverage, and high access to information as important determinants of adaptive capacity. A universal-coverage health care system and a high level of access to information are judged to be equivalent to $12,000-$14,000 in per capita income. We use the estimated coefficients and country sociodemographics to construct an index of adaptive capacity for several countries. In panel-data regressions, this index is a good predictor of mortality in climatic disasters, even after controlling for other determinants of sensitivity and exposure, and for per capita income. We conclude that our conjoint choice questions provide a novel and promising approach to eliciting expert judgments in the climate change arena.

Climate change and malaria: analysis of the SRES climate and socio-economic scenarios

The distribution and seasonal transmission of malaria is affected by climate, as both vector and parasite are sensitive to temperature. A global model of malaria transmission has been developed to estimate the potential impact of climate change on seasonal transmission and populations at risk of the disease (MIASMA v.2.2). “Population at risk” is defined as the population living in areas where climate conditions are suitable for malaria transmission. This assessment describes model simulations driven by the latest scenarios from the IPCC. The climate scenarios were derived from the Hadley Centre model HadCM3 runs with four SRES emissions scenarios: A1FI, A2, B1 and B2. The additional population at risk was determined under each of the SRES population scenarios by downscaling national estimates to the 0.5×0.5° scale grid and re-aggregating by region. Additional population at risk due to climate change are projected in East Africa, central Asia, China and areas around the southern limit of the distribution in South America. Decreases in the transmission season are indicated in many areas where reductions in precipitation are projected by the Hadley Centre model, such as the Amazon and in Central America. The outcomes of the malaria model are sensitive to (1) spatial distribution of precipitation projections and (2) population growth in those areas where there is new risk due to climate change. This paper describes a new method for describing vulnerability to the potential impacts of climate change. Countries were classified according to their current vulnerability and malaria control status using expert judgement. This vulnerability incorporates both socio-economic status, as a measure for adaptive capacity, and climate as malaria at the fringes of its climate-determined distribution is easier to control than malaria in tropical endemic regions. Thus, current malaria control status is used as an indicator of adaptive capacity. For those countries that currently have a limited capacity to control the disease, the model estimates additional populations at risk by 2080s in the range of 90 m (A1FI) to 200 m (B2b). The greatest impact under B2 reflects population growth in risk areas in Eurasia and Africa. Climate-induced changes in the potential distribution of malaria is projected in the poor and vulnerable regions of the world. However, climate change is not likely to affect malaria transmission in the poorest countries where the climate is already highly favourable for transmission.

Disabling effects of chronic disease and impairment—II. Functional capacity limitations

This study has examined the effect of functional capacity limitations on the severity of disability. Ideally, measures of disability take into account not only residual impairments and capacity limitations, but also residual capacities and adaptive capabilities of the individual. Personal, social and vocational factors affect individual capacity for adaptation to functional limitations. Attributes such as age, sex, education, and occupational experience, for example, are indicators of residual and adaptive capacity. Women, older people, the under-educated, and the unskilled appear to have less adaptive capacity or to face more demanding requirements; their ability to work was more affected by activity limitations. Young disabled people were more likely to adapt to work limitations than older people, regardless of extent of functional limitation; relatively slight functional limitations had more effect on the work capacity of women than of men; men appear to be more able than women to cope with impairment by accommodations on the job. The social consequence of disability were also directly related to activity and independence limitations. Work, household and social participation declined with increased functional limitations; reduced participation was closely reflected in increased severity of disability. The Functional Limitations Index also showed the selectivity of the public wage replacement and income support programs in relationship to disability criteria. Social security disability beneficiaries, for example, were concentrated among those with more severe activity limitations. The program was highly selective in terms of functional limitations and severity of disability. Entitlement to other income-maintenance programs tended to be less closely associated with functional limitations; program entitlement, however, was more closely related to functional limitation than to severity of disability. These findings clearly indicate that functional limitations are a primary consideration in the evaluation of disability. The index of functional limitations developed in this study showed a close relationship between the severity of disability and functional limitations. Data on activity limitations provide better prediction of the severity of disability than impairment indicators, such as chronic conditions or orthopedic aids. In combination with social indicators of capacity and adaptability, functional limitations indicators provide useful measures for the examination and prediction of disability and of behavioral changes in family and work activity.