Climate Vulnerability System – An Urban Planning Tool

Global and regional climate scenarios point to the risk

of climate change for Brazilian states and cities

(Marengo, 2011). There is a growing awareness on

the Brazilian government, academia, and society

about the need to build strategies to reduce its

danger. In 2010, Congress passed the National

Climate Change Policy Law (PNMC Brazil – Law Nº

12.187/2009). It called for the reduction of

greenhouse gas and the adoption of adaptation

strategies.

In order to design effective adaptation strategies and

prioritize resource investment, it is critical to know

how vulnerable a given population is to climate

change. Therefore, our research efforts are

concentrated in design MIPVCC for the Brazilian states

marked in Figure 1. (Barata, MML et al, 2011, 2014,

2015; Confalonieri, U. et al, 2017, 2018; Quintão, AF,

2017).

INTRODUCTION

Our research team is engaged in developing composite indicators to measure and evaluate the relative

vulnerability of municipal population towards climate change. It aims to foster the creation of strategies that weaken,

over time, the potential negative effects of climate change on municipal population.

The Municipal Index of Population Vulnerability to Climate Change (MIPVCC) is achieved using official secondary data

bearing in mind the three components that represent an integrated vulnerability concept, according to IPCC framework

- exposure, sensitivity and adaptive capacity. Those data are aggregated through a Climate Vulnerability System (CVS)

that automatically calculates the MIPVCC and its components, builds thematic maps and allows the update and

insertion of new data so that the index can always be up to date. It is an useful tool for planning and monitoring local

adaptation strategies.

Martha Barata; Felipe Vommaro; Diana Marinho; Frederico de Oliveira; Heliana V. Silva

METHOD

CONCEPTUAL MODEL OF MPVICC

The MIPVCC focuses on quantitative estimates applied to compare the population vulnerability between the

municipalities inside each State. It is also concerned with adding new scenarios of climate change in order to determine

the municipality most exposed and vulnerable to climate hazards.

MIPVCC

Municipal Population Vulnerability Index to

Climate Change

CCI Climate Scenarios Index

(Anomaly Precipitation and Temperature = Hazard Fator)

ACI

Adaptation Capacity Index

EI

Exposition Index

MIPV

Municipal PopulationVulnerability Index

SISensibility

Index

The process of generating the MPVCCI is repetitive and its calculation is complex. It contains at least 04 macro

indexes (EI, SI, CAI and CCI) and approximately 30 indicators. Their many sources of information are different

and regularly updated. In this context, calculation using manual process is slow, error prone and inefficient.

The CVS should be a facilitator, which automates the calculation of the indexes and the generation of thematic

maps of the MIPVCC and its macro-indexes.

It allows updating the data in the CVS database in order to:

• Keep the MIPVCC and macro-indexes updated

• Monitor the evolution of those indexes over the years

The CVS is constructed with free software components.

BUILDING AND READING MIPVCC

Exposition, sensibility, adaptation capacity and climate scenario index are normalized for being aggregated in MIPV and in MIPVCC.

(PVI of the Municipality – Lower PVI between Municipalities)

In = _____________________________________________________________

(Higher PVI between Municipalities - Lower PVI between Municipalities)

In = Normalized Index

PVI = Vulnerability Index of Dimension X

X = Exposition, Sensitivity or Adaptation Capacity

After normalizing the index, values ranged from zero to one, where the municipalities with a zero index were the least vulnerable, those with a one were the most vulnerable, and the others ranged somewhere in between.

WHY CVS?

MIPVn = EIn + SIn + ACIn

3

MIPVCCn = MIPVn + CCIn

2

READING AND APPLYING MIPVCC PER CVS

Examples of some of the outputs of CVS are presented here.

The distribution of population vulnerability (MIPV) to climate change in the state of Maranhão/Brazil is presented and we observe

that the relative distribution changes when we consider the Regional Climate Change Scenario (ETA- HADGEM).

In both maps we observe that the population of the city of Santa Luzia is the most vulnerable, so stakeholders should start

focusing in the sensibility and exposition sphere of their population when they plan the reduction of their vulnerability.

MIPV in MaranhãoMIPVCC in Maranhão

APPLYING MIPVCC AND CVS IN CITIES

This pragmatic approach is considered useful to plan and monitor the results of adaptation strategy for Brazilian states and it can it be

tailored to be an urban planning tool. It is important to consider the following challenges, when building the tool:

• Select and tailor the appropriate indicators,

• Collect sufficient official data (data gap),

• Engage the municipal stakeholders in the process,

• Permanency of their use over time

SIACI

EI

Participation of IE, IS and ICA in MIPV in Santa Luzia, Maranhão

REFERENCES

Barata MML. et al, 2011, 2014. Study of population vulnerability to climate change in the municipalities of the State of Rio de Janeiro. View athttp://www.fiocruz.br/ioc/media/20150722_Relatorio_Final_RJ.pdf .

Barata, MML. et al., “Mapa de vulnerabilidade da população do estado do Rio de Janeiro aos impactos das mudanças climáticas,” in Metodologia de Estudos Vulnerabilidade de Mudança do Clima no Brasil, C. M. Globais and M. Chang, Eds., vol. 5 of Instituto Virtual Internacional de Mudanças Globais/COPPE-Universidade Federal do Rio de Janeiro, chapter 4, pp. 63–90, Interciência, Rio de Janeiro, Brazil, 1st edition, 2015. View at Google Scholar;

Barata, MML.et al., “Estudo da vulnerabilidade socioambiental da população dos municípios baianos inseridos na bacia hidrográfica do Rio São Francisco no bioma Caatinga, aos impactos das mudanças climáticas,” Research Report, Fiocruz, Rio de Janeiro, Brasil, 2015.View at Google Scholar or athttp://www.fiocruz.br/ioc/media/Estudo_de_Vulnerabilidade_Bahia.pdf .

Confalonieri, U. et al., “Vulnerabilidade Climática no Brasil,” in Metodologia de Estudos Vulnerabilidade de Mudança do Clima no Brasil, M. Chang, K. Goés, L. Fernandes, M. A. V. Freitas, and L. P. Rosa, Eds., vol. 5 of Instituto Virtual Internacional de Mudanças Globais/COPPE-Universidade Federal do Rio de Janeiro, chapter 2, pp. 25–38, Interciência, Rio de Janeiro, Brasil, 1st edition, 2015. View at Google Scholar

Quintão, A. F. et al. , 2017. Social, Environmental, and Health Vulnerability to Climate Change: The Case of the Municipalities of Minas Gerais, Brazil.Journal of Environmental and Public Health. Volume 2017. Article ID 2821343 .

Confalonieri, U. et al, 2018. Vulnerability indicators for monitoring adaptation actions to climate change in Brazil. Gran: Brazilian Climate Fund under the auspices of its Environmental Ministry..www.projetovulnerabilidade.fiocruz.br.

Brazilian Climate Fund under the auspices of its Environmental MinistryCAPES: process 1736/2015Health Ministry of Brazil FundState Fund for Environmental Conservation and Urban Development of Rio de Janeiro

AKNOWLEDGMENT

Figure 1: MIPVCC for Brazilian States

Santa Luzia – 1,00

ENGAGING STRATEGIC STAKEHOLDER (POLICE MAKER. SCIENTISTS AND PRACTIONERS)IN THE PROCESS OF CONSTRUCTION AND USE MPIVCC AND CVS

Santa Luzia – 1,00

STUDY RATIONALE