Assessing urban adaptive capacity to climate change

Systemic inequalities in heat risk for greater London

The temperature rise and increases in extreme heat events related to global climate change is a growing public health threat. Populations in temperate climates, including the UK, must urgently adapt to increased hot weather as current infrastructure primarily focusses on resilience to cold. As we adapt, care should be taken to ensure existing health inequalities are reduced. Lessons can be learned from regions that experience warmer climates and applied to adaptation in the UK. We identified known indicators of heat-health risk and explored their distribution across area level income for London. Understanding these indicators and their distributions across populations can support the development of interventions that have the dual aim of improving health and reducing inequalities. An exploratory analysis was conducted for each indicator at neighbourhood level to assess existence of disparities in their distributions across London. A systems-thinking approach was employed to deduce if these amount to systemic inequalities in heat risk, whereby those most exposed to heat are more susceptible and less able to adapt. Using this information, we proposed interventions and made recommendations for their implementation. We find inequalities across indicators relating to exposure, vulnerability, and adaptive capacity. Including inequalities in urban greening and access to greenspace, physical and mental health and access to communication and support. Through a system diagram we demonstrate how these indicators interact and suggest that systemic inequalities in risk exist and will become more evident as exposure increases with rising temperatures, depending on how we adapt. We use this information to identify barriers to the effective implementation of adaptation strategies and make recommendations on the implementation of interventions. This includes effective and wide-reaching communication considering the various channels and accessibility requirements of the population and consideration of all dwelling tenures when implementing policies relating to home improvements in the context of heat.

Evolution of temperature-attributable mortality trends looking at social inequalities: An observational case study of urban maladaptation to cold and heat

BACKGROUND

To date, little is known about the temporal variation of the temperature-mortality association among different demographic and socio-economic groups. The aim of this work is to investigate trends in cold- and heat- attributable mortality risk and burden by sex, age, education, marital status, and number of household occupants in the city of Turin, Italy.

METHODS

We collected daily time-series of temperature and mortality counts by demographic and socio-economic groups for the period 1982-2018 in Turin. We applied standard quasi-Poisson regression models to data subsets of 25-year moving subperiods, and we estimated the temperature-mortality associations with distributed lag non-linear models (DLNM). We provided cross-linkages between the evolution of minimum mortality temperatures, relative risks of mortality and temperature-attributable deaths under cold and hot conditions.

RESULTS

Our findings highlighted an overall increase in risk trends under cold and heat conditions. All-cause mortality at the 1st percentile increased from 1.15 (95% CI: 1.04; 1.28) in 1982-2006 to 1.24 (95% CI: 1.11; 1.38) in 1994-2018, while at the 99th percentile the risk shifted from 1.51 (95% CI: 1.41; 1.61) to 1.59 (95% CI: 1.49; 1.71). In relation to social differences, women were characterized by greater values in respect to men, and similar estimates were observed among the elderly in respect to the youngest subgroup. Risk trends by educational subgroups were mixed, according to the reference temperature condition. Finally, individuals living in conditions of isolation were characterized by higher risks, with an increasing vulnerability throughout time.

CONCLUSIONS

The overall increase in cold- and heat- related mortality risk suggests a maladaptation to ambient temperatures in Turin. Despite alert systems in place increase public awareness and improve the efficiency of existing health services at the local level, they do not necessarily prevent risks in a homogeneous way. Targeted public health responses to cold and heat in Turin are urgently needed to adapt to extreme temperatures due to climate change.

Urban heat vulnerability: A dynamic assessment using multi-source data in coastal metropolis of Southeast China

Extreme heat caused by global climate change has become a serious threat to the sustainable development of urban areas. Scientific assessment of the impacts of extreme heat on urban areas and in-depth knowledge of the cross-scale mechanisms of heat vulnerability forming in urban systems are expected to support policymakers and stakeholders in developing effective policies to mitigate the economic, social, and health risks. Based on the perspective of the human-environment system, this study constructed a conceptual framework and index system of "exposure-susceptibility-adaptive capacity" for urban heat vulnerability (UHV) and proposed its assessment methods. Taking Xiamen City, a coastal metropolis, as an example, spatial analysis and Geodetector were used to explore the spatial and temporal changes, spatial characteristics, and patterns of UHV under multiple external disturbances from natural to anthropological factors, and to reveal the main factors influencing UHV forming and spatial differentiation. Results showed that the exposure, susceptibility, adaptive capacity, and UHV in Xiamen City had a spatial structure of "coastal-offshore-inland". On the hot day, both the exposure and UHV showed a temporal pattern of "rising and then falling, peaking at 14:00" and a spatial pattern of "monsoonal-like" movement between coast and inland. Coastal zoning with favorable socioeconomic conditions had less magnitude of changes in UHV, where the stability of the urban system was more likely to be maintained. During the hot months, the high UHV areas were mainly distributed in the inland, while coastal areas showed low UHV levels. Further, coastal UHV was mainly dominated by "heat exposure", offshore by "comprehensive factors", and inland in the northern mountainous areas by "lack of adaptive capacity". Multi-scale urban adaptive capacity was confirmed to alter spatial distribution of exposure and reshape the spatial pattern of UHV. This study promotes the application of multi-scale vulnerability framework to disaster impact assessment, enriches the scientific knowledge of the urban system vulnerability, and provides scientific references for local targeted cooling policy development and extreme heat resilience building programs.

Determinant Indicators for Assessing the Adaptive Capacity of Agricultural Producers to Climate Change

Assessing adaptive capacity to climate change is a complex task since it is a multidimensional component. There has been considerable discrepancy between the dimensions or elements that compose it. This study aimed to analyze the relevant dimensions and indicators that allow estimation of the adaptive capacity to climate change and to propose a set of indicators that will enable their application to assessment at the level of agricultural producers. A systematic review of scientific literature on evaluating or measuring adaptive capacity to climate change was carried out. Subsequently, the indicators were analyzed and selected through a coincidence analysis and were calibrated through a multicriteria evaluation with relevant actors in the southern Mexico, state of Chiapas. In total, 329 indicators were identified and analyzed. As a result, 19 indicators were selected and then grouped into six dimensions: economic resources, human resources, infrastructure for production and marketing, institutionality, social capital, and natural resources. These represent the 14 specific dimensions with the greatest potential to contribute to the estimation of adaptive capacity to climate change. The dimensions and indicators can be applied to assess the adaptive capacity of farmers in Mexico at a national or regional scale and specifically by producer types.

Heat Adaptive Capacity: What Causes the Differences Between Residents of Xiamen Island and Other Areas?

Extreme heat events caused by climate change have serious adverse effects on residents' health in many coastal metropolises in southeast China. Adaptive capacity (AC) is crucial to reduce heat vulnerability in the human-environment system. However, it is unclear whether changes in individual characteristics and socioeconomic conditions likely amplify or attenuate the impacts of residents' heat adaptive capacity (HAC) changes. Moreover, which public policies can be implemented by the authorities to improve the HAC of vulnerable groups remains unknown. We conducted a questionnaire survey of 630 residents of Xiamen, a typical coastal metropolis, in 2018. The effects of individual and household characteristics, and government actions on the residents' HAC were examined by using ordinal logistic regression analysis. Results show that the majority (48.10%) of Xiamen residents had a "medium" HAC level, followed by a "high" level (37.14%). On Xiamen Island, residents who settled locally for one-three years and spent less than one hour outdoors might report weaker HAC, and their HAC would not improve with increased air conditioning units in household. In other areas of Xiamen, residents with more rooms in their households, no educational experience, and building areas <50 m2 might report better HAC. Further, vulnerable groups, such as local residents and outdoor workers on Xiamen Island, people lacking educational experience and renters in other areas of Xiamen, showed better AC to hot weather than those in previous studies. Low-income groups should be given more attention by local governments and community groups as monthly household income played a positive role in improving Xiamen residents' HAC. Rational green spaces planning and cooling services, such as street sprinkling operations, provided by municipal departments can effectively bring benefits to Xiamen residents. Identification of basic conditions of AC has significant implications for practical promoting targeted measures or policies to reduce health damages and livelihood losses of urban residents during extreme heat events.

Assessment of Cities’ Adaptation to Climate Change and Its Relationship with Urbanization in China

Global climate change has led to more frequent occurrences of extreme, dangerous events; therefore, it is urgent to enhance cities’ adaptation to climate change. Focusing on the impact of high temperature, low temperature, drought, and flooding, we established multi-dimensional assessment systems covering natural, economic, and social elements for cities’ climate change adaptations. Based on the assessment systems, studies on adaptation to climate change were conducted in 248 cities in China using the entropy weight method, coefficient of variation method, and exploratory spatial data analysis; with the help of geographically weighted regression (GWR), the relationships between four types of urbanization and cities’ adaptation to climate events were explored. The results showed the following: (1) High-administrative-level cities had higher adaptation than ordinary prefecture-level cities. (2) The differences in adaptation to the four types of climate events between cities within each of the seven regions in China presented significantly different spatial patterns. (3) Under the four types of climate events, the global spatial correlations of cities’ adaptations in China were positive. The agglomeration characteristics of adaptation were mainly H–H and L–L agglomerations. (4) When analyzing the impacts of four types of urbanization on cities’ adaptation to climate events, the fitting effects of GWR models were far better than those of OLS models. Population urbanization, economic urbanization, land urbanization, and industrial urbanization had different impacts on adaptation. Under the influence of social and economic development, the urbanization regression coefficients of different cities had significant spatial differences.

Exploring Water Landscape Adaptability of Urban Spatial Development Base on Coupling Coordination Degree Model A Case of Caidian District, Wuhan

Under the background of rapid urbanization, the study explored the adaptive relationship between urban spatial development and water landscape in different stages in Caidian District, Wuhan in order to reveal the characteristics and influencing factors of water landscape adaptability of urban spatial development and improve urban sustainability aiming at optimizing spatial development and improving water landscape function. Caidian District was seen as a complex adaptive system formed by the interaction between the base layer and the occupation layer. The assessment system of urban spatial development is constructed based on land use data and landscape pattern indices, and the coupling coordination degree model and correlation analysis were used to describe the characteristics of water landscape adaptability of urban spatial development. The results showed that: (1) the adaptation relationship between urban space and water landscape in Caidian District was becoming tense; (2) different spatial systems have unique adaptation cycles to water landscape; (3) the 1000 m buffer is the main area affected by water landscape adaptability of urban spatial development. This study provides a new perspective for the urban adaptability. Finally, improvement suggestions were put forward by three aspects of water landscape structure control, urban development control, and ecological space demarcation.

Social inequalities in heat-attributable mortality in the city of Turin, northwest of Italy: a time series analysis from 1982 to 2018

BACKGROUND

Understanding context specific heat-health risks in urban areas is important, especially given anticipated severe increases in summer temperatures due to climate change effects. We investigate social inequalities in the association between daily temperatures and mortality in summer in the city of Turin for the period 1982-2018 among different social and demographic groups such as sex, age, educational level, marital status and household occupants.

METHODS

Mortality data are represented by individual all-cause mortality counts for the summer months between 1982 and 2018. Socioeconomic level and daily mean temperature were assigned to each deceased. A time series Poisson regression with distributed lag non-linear models was fitted to capture the complex nonlinear dependency between daily mortality and temperature in summer. The mortality risk due to heat is represented by the Relative Risk (RR) at the 99th percentile of daily summer temperatures for each population subgroup.

RESULTS

All-cause mortality risk is higher among women (1.88; 95% CI = 1.77, 2.00) and the elderly (2.13; 95% CI = 1.94, 2.33). With regard to education, the highest significant effects for men is observed among higher education levels (1.66; 95% CI = 1.38, 1.99), while risks for women is higher for the lower educational level (1.93; 95% CI = 1.79, 2.08). Results on marital status highlighted a stronger association for widower in men (1.66; 95% CI = 1.38, 2.00) and for separated and divorced in women (2.11; 95% CI = 1.51, 2.94). The risk ratio of household occupants reveals a stronger association for men who lived alone (1.61; 95% CI = 1.39, 1.86), while for women results are almost equivalent between alone and not alone groups.

CONCLUSIONS

The associations between heat and mortality is unequal across different aspects of social vulnerability, and, inter alia, factors influencing the population vulnerability to temperatures can be related to demographic, social, and economic aspects. A number of issues are identified and recommendations for the prioritisation of further research are provided. A better knowledge of these effect modifiers is needed to identify the axes of social inequality across the most vulnerable population sub-groups.

Constructing a comprehensive disaster resilience index: The case of Italy

Measuring disaster resilience is a key component of successful disaster risk management and climate change adaptation. Quantitative, indicator-based assessments are typically applied to evaluate resilience by combining various indicators of performance into a single composite index. Building upon extensive research on social vulnerability and coping/adaptive capacity, we first develop an original, comprehensive disaster resilience index (CDRI) at municipal level across Italy, to support the implementation of the Sendai Framework for Disaster Risk Reduction 2015–2030. As next, we perform extensive sensitivity and robustness analysis to assess how various methodological choices, especially the normalisation and aggregation methods applied, influence the ensuing rankings. The results show patterns of social vulnerability and resilience with sizeable variability across the northern and southern regions. We propose several statistical methods to allow decision makers to explore the territorial, social and economic disparities, and choose aggregation methods best suitable for the various policy purposes. These methods are based on linear and non-liner normalization approaches combining the OWA and LSP aggregators. Robust resilience rankings are determined by relative dominance across multiple methods. The dominance measures can be used as a decision-making benchmark for climate change adaptation and disaster risk management strategies and plans.

Comparing adaptive capacity index across scales: The case of Italy

Measuring adaptive capacity as a key component of vulnerability assessments has become one of the most challenging topics in the climate change adaptation context. Numerous approaches, methodologies and conceptualizations have been proposed for analyzing adaptive capacity at different scales. Indicator-based assessments are usually applied to assess and quantify the adaptive capacity for the use of policy makers. Nevertheless, they encompass various implications regarding scale specificity and the robustness issues embedded in the choice of indicators selection, normalization and aggregation methods. We describe an adaptive capacity index developed for Italy's regional and sub-regional administrative levels, as a part of the National Climate Change Adaptation Plan, and that is further elaborated in this article. The index is built around four dimensions and ten indicators, analysed and processed by means of a principal component analysis and fuzzy logic techniques. As an innovative feature of our analysis, the sub-regional variability of the index feeds back into the regional level assessment. The results show that composite indices estimated at higher administrative or statistical levels neglect the inherent variability of performance at lower levels which may lead to suboptimal adaptation policies. By considering the intra-regional variability, different patterns of adaptive capacity can be observed at regional level as a result of the aggregation choices. Trade-offs should be made explicit for choosing aggregators that reflect the intended degree of compensation. Multiple scale assessments using a range of aggregators with different compensability are preferable. Our results show that within-region variability can be better demonstrated by bottom-up aggregation methods.

A spatial fuzzy logic approach to urban multi-hazard impact assessment in Concepción, Chile

Even though most cities are exposed to more than one hazard, local planners and decision-makers still have a limited understanding of the exposure and sensitivity to and the spatial distribution of hazards. We examine the impact of multiple hazards in the Concepción Metropolitan Area (CMA), Chile. A flexible methodology based on spatial fuzzy logic modelling was developed to explore the impact of weather-related hazards, including coastal flooding, fluvial flooding, water scarcity, heat stress, and wildfire. 32 indicators were standardised and then aggregated through a stepwise approach into a multi-hazard impact index. We find that all the municipalities in the CMA increased their level of impact between 1992 and 2002, due to a larger increase in the exposure rather than the modest decrease in sensitivity. Municipal sensitivity was driven mostly by changes in the population's age structure. Wildfires and water scarcity appeared to have the largest impact on all municipalities. Fuzzy modelling offered high flexibility in the standardisation and aggregation of indicators with diverse characteristics, while also providing a means to explore how the interaction of numerous indicators influenced the index. The resulting maps can help identify indicators, components, and hazards or combinations of hazards that most influence the impact on municipalities. The results can be used to improve and promote dialogue among policy-makers and stakeholders regarding prioritisation of resources for urban development in ways that can also reduce exposure and sensitivity and lower vulnerability to climate change. The methods presented can be adapted to other cities.