Advanced Operationalization Framework for Climate-Resilient Urban Public Health Care Services: Composite Indicators-Based Scenario Assessment of Khon Kaen City, Thailand
Abstract
:1. Introduction
2. Materials and Methods
2.1. Scenarios Storyline Development
2.2. Composite Indicators Development
2.2.1. Indicators Identification
2.2.2. Normalization and Aggregation
- Sub-indicator layer: Input data were derived from both the public health care facilities’ questionnaire survey and the given scenario storylines. Distance to target normalization was applied for representing a fraction of the highest target value, which conserves the proportion and trackability of the original data. All sub-indicators under an indicator were calculated with equal-weighted arithmetic (additive) aggregation to represent an indicator.
- Indicator level: An indicator represents a value of at least one sub-indicator or an average value of many sub-indicators. Conceptually, individual indicators shall have a different degree of influence on the outcome of the potential impact pillar to which it belongs. Therefore, proportionately weighted normalization was used for the indicator layer; a single attribute value was divided by the sum total of the values of attributes. Weighted values can be assigned based on the experts’ judgement (hazards and vulnerability-related indicators) and equal-weighted (exposure-related indicators). In terms of aggregation scheme selection, there is a possibility that at least one of the indicator’s values may contain “0”; hence, arithmetic (additive) aggregation is operated.
- Pillar level: Based on the IPCC climate risk concept [18], the potential impact is a multiplication result of hazard, exposure, and vulnerability pillars. Therefore, the absence of one pillar or being assigned as 0 in one of the terms meant no potential impact occurred. To this connection, the geometric (multiplicative) aggregation method is executed for this non-compensability relationship among the pillars. In this regard, the possibility of potential impact value derived from the set of the composite indicators must be present in absolute terms between 0 to 1. A value of 0 means no potential impact, and 1 is the possible worst potential impact.
2.3. Questionnaire Survey and Data Analysis
2.4. Sensitivity Analysis
3. Results
3.1. Study Area—Khon Kaen City
3.2. Khon Kaen City Thailand in 2037
3.2.1. Trend Scenario Storyline
3.2.2. Desirable Scenario Storyline
3.3. Composite Indicators Development
3.3.1. Sensitivity
3.3.2. Coping Capacity and Adaptive Capacity
3.4. Dataset and Normalization
3.5. Potential Impact Analysis
3.6. Sensitivity Analysis
4. Discussion
4.1. Composite Indicators-Based Scenario Assessment—A Novel Tool for Climate-Resilient Health Care Services
4.2. Constraints and Scalability Opportunities
4.2.1. Sectoral Benchmarking in Needs
4.2.2. Data Constraints and Statistical Conciseness
- Balancing the number of indicators
- Statistical and Theoretical Convergence
- Quality of Input Data
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Composite Indicator Layers | Aggregation Schemes | Weighting Schemes | Normalization Schemes |
---|---|---|---|
Pillar | Multiplicative | Equal weight | Proportionate normalization |
Indicator | Additive | Equal weight and Expert weight | Proportionate normalization |
Sub-indicator | Additive | Equal weight | Distance to target normalization |
Indicators | Trend Scenario | Desired Scenario | ||
---|---|---|---|---|
Area-Based | Service Network | Area-Based | Service Network | |
H1: Fluvial flood | 0.588 | 0.639 | 0.588 | 0.639 |
H2: Pluvial flood | 1.279 | 1.279 | 1.279 | 1.279 |
H3: Water scarcity | 1.442 | 1.442 | 1.442 | 1.442 |
Weighted normalized Hazard | 0.827 | 0.840 | 0.827 | 0.840 |
E1: Exposure of public health facility’s building(s) | 1.040 | 1.017 | 1.040 | 1.017 |
E2: Exposure of working systems | 1.594 | 1.178 | 0.263 | 0.279 |
Weighted normalized Exposure | 0.659 | 0.549 | 0.326 | 0.324 |
V1: Over carrying capacity | 0.800 | 0.800 | 0.480 | 0.710 |
V2: Variety of vulnerable patients | 0.683 | 0.778 | 0.683 | 0.778 |
V3: Resource insufficiency | 0.624 | 0.608 | 0.440 | 0.555 |
V4: Poor system conditions and maintenance of essential working systems | 0.064 | 0.033 | 0.000 | 0.000 |
V5: Downtime of essential working systems | 0.053 | 0.045 | 0.053 | 0.045 |
V6: Flexibility and modularity | 0.456 | 0.480 | 0.000 | 0.000 |
V7: Diversity of suppliers | 0.217 | 0.122 | 0.047 | 0.031 |
V8: Redundancy | 0.416 | 0.397 | 0.081 | 0.097 |
V9: Responsiveness | 0.626 | 0.559 | 0.000 | 0.000 |
V10: Resource mobilization | 0.359 | 0.535 | 0.000 | 0.000 |
V11: Integration and coordination | 0.300 | 0.084 | 0.000 | 0.000 |
V12: Information | 0.388 | 0.424 | 0.000 | 0.000 |
V13: Preparedness and risk transfer | 0.720 | 0.594 | 0.000 | 0.000 |
V14: Participation and inclusiveness | 0.742 | 0.808 | 0.000 | 0.000 |
V15: Capacity development | 0.642 | 0.167 | 0.000 | 0.000 |
V16: Mainstreaming climate-risk in planning process | 0.440 | 0.487 | 0.000 | 0.000 |
V17: Monitoring and evaluation | 0.300 | 0.323 | 0.000 | 0.000 |
Weighted normalized Vulnerability | 0.461 | 0.426 | 0.105 | 0.130 |
Potential impact = HxExV Very low (0.000–0.016) Low (>0.016–0.125) Medium (>0.125–0.422) High (>0.422–1.000) | 0.251 | 0.196 | 0.028 | 0.035 |
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Puntub, W.; Greiving, S. Advanced Operationalization Framework for Climate-Resilient Urban Public Health Care Services: Composite Indicators-Based Scenario Assessment of Khon Kaen City, Thailand. Int. J. Environ. Res. Public Health 2022, 19, 1283. https://doi.org/10.3390/ijerph19031283
Puntub W, Greiving S. Advanced Operationalization Framework for Climate-Resilient Urban Public Health Care Services: Composite Indicators-Based Scenario Assessment of Khon Kaen City, Thailand. International Journal of Environmental Research and Public Health. 2022; 19(3):1283. https://doi.org/10.3390/ijerph19031283
Chicago/Turabian StylePuntub, Wiriya, and Stefan Greiving. 2022. "Advanced Operationalization Framework for Climate-Resilient Urban Public Health Care Services: Composite Indicators-Based Scenario Assessment of Khon Kaen City, Thailand" International Journal of Environmental Research and Public Health 19, no. 3: 1283. https://doi.org/10.3390/ijerph19031283