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Comparative analysis for changes in the provision of Ecosystem Services and
Well-being in Brazilian hotspots (Atlantic Forest and Cerrado) in the Sao Paulo
State.
Análise comparativa das mudanças na provisão de Serviços Ecossistêmicos e
bem-estar, em hotspots Brasileiros (Mata Atlântica e Cerrado) no estado de São
Paulo
Fapesp Project N0 2014/05741-1Research Program on Global Environmental Change (PPMCG)
Patricia F. Pinho, PhDInterdisciplinary Climate Change Program (INCLINE) – University of São
Paulo
Visiting Scholar Geosciences – UoE
Email: [email protected]
Research WPs
Commodities Agriculture Expansion & Protected Areas
30% Brazil GDP &
Agriculture annual loss of US$ 4 billions to2020, but sugarcane increases itsproductivity (Assad, Pinto 2008).
~ 45 millions people
Protected areas of São Paulo amongst mostvulnerable socioeconomic and ecologically(Ribeiro et al., 2009)
Climate Change impacting socioeconomicsystems (Ambrizzi et al 2008)
What are the impact of these dynamics on people’s wellbeing ,
protected areas and agricultural land?
Hypothesis
Climatic variabilitythresholds
Adaptation
capacity
vuln
erab
ility
Source: Adapted from ACES/ESPA Project
Study Area
Micro-regions of the São Paulo State
São Paulo State
• Carbon Balance Assessments (quanti-quali analysis)
• Forest Cover Maps
• Socio- climatic indices
• Sustainability Barometer
Regional
• Survey Instruments
• Structured interviews
• Participatory approach
Local
• Bridge regional and local scales
• Integrative workshop
• Climate Resilient Development linked to Sustainable Development Goals (SDGs)
Relevance on ES, Wellbeing and
Climate Change
Production Basic profile
Ecosystem Services
Wellbeing
Governance and
production
Climate change
Governance and Legal Reserves
Happiness
Scales and Actors in relation to ES, Wellbeing and Climate Change
Corumbataí
Monteiro Lobato
Cananéia
Results WP 2: Forest Cover and Fragments Maps
7.5% of Forest Cover
76 fragments
31.3% of Forest Cover
77 fragments
81,2% of Forest Cover
198 fragments
Maps: Adrian Gonzalez
Projeções de
Mudanças de
Temperatura
do ar próximo
a superfície
(οC)
2021 – 2050
Modelos
Climáticos
integrantes do
IPCC AR5
Características dos
Cenários
Muito Otimista
Otimista
Mais Realista
Pessimista
Projeções dos Modelos Climáticos do IPCC AR5 - Temperatura
Índice de Vulnerabilidade Sócio-Climática (SCVI)
Sensibilidade
Capacidade AdaptativaExposição
RCCI (CMIP5)
Densidade Pop: 2010
IDHM: 2010
São PauloImpactos Climáticos Projetados (2021-2050 &
2071-2100)
Prob Conf
Temp. Média (+1.5 a +2.0οC & +2.0 a +5.0οC) 3 ***
Temp. Mínima (+1.0 a +2.5οC & +1.0 a +4.5οC) 3 ***
Temp. Máxima (+1.0 a +3.0οC & +1.0 a +5.0οC) 3 ***
Número de dias frios (diminuição) 3 **
Número de noites frias (diminuição) 3 **
Número de dias quentes (aumento) 3 **
Número de noites quentes (aumento) 3 **
Precipitação (-5 & -10% IPCC) 1 *
Aumento no número de dias secos consecutivos 3 **
Aumento da irregularidade na distribuição da
precipitação ao longo do ano
3 **
Aumento na frequência de secas/estiagem 2 *
Vulnerabilidade Sócio-climática (em comparação ao
Brasil como um todo) = ALTA
3 *
Estresse Térmico = Aumento 2 **
Nível de confiabilidade
*** Confiabilidade Alta
** Confiabilidade Média
* Confiabilidade Baixa
Prob. de
ocorrênciaClassificação
- Sem risco ou Incerto
1 Pouca probabilidade (0 - 20%)
2 Probabilidade média (20 - 60%)
3 Probabilidade alta (60 - 100%)
Prescott-Allen, 1997
Measu
res
of
Su
sten
tab
ilit
y
Axis
Human Wellbeing
BEH
Axis
Ecosystem Wellbeing
BEE
TH
EM
ES
IND
ICA
TO
RS
IND
ICE
S
Regional Scale
Methods for Sustainability Barometer
We conducted a linear interpolation adjusting the local scale with
the Barometer scale (Lameira et al., 2015) as:
Onde:EB – Barometer Scale;EB1 - Barometer Scale(Valor inicial do intervalo da Escala do Barômetro);EB2 – Barometer Scale (Valor final do intervalo da Escala do Barômetro);EB - Local Scale (Study areas); EL1 – Local Scale ( initial value of sustainability defined by the study area); EL2 - Local Scale (Final value of the sustainability range defined for the study area);VR – Real Value (value of the indicator observed in each municipality);
Environmental (land use change, forest cover, special together
with geoprocessing analysis.
𝐵𝑆𝑥 = 𝐸𝐿1 − 𝑉𝑅 𝑥 𝐸𝐵1 − 𝐸𝐵2
𝐸𝐿1 − 𝐸𝐿2 𝑥 −1 + 𝐸𝐵1
Sustainable Barometer
Bi-Dimensional matrix
Dimensões Cananéia Corumbataí Monteiro Lobato Rio Claro
BEH 57,08 56,87 48,55 62,53
BEE 69,43 39,15 34,57 39,83
Interviews
Policy
(Institutes, Secretaries, Municipal body)
Local Communities
Farmers, Traditional Communities, rural cooperatives
Elicitation workshop
Stakeholders
Science, Policy and Local communities
ES importance
Wellbeing importance
Climate Change
Science
WP 4 : Survey Instrument and Participatory Approach
(n=114)
materialsubjectiverelational
Convergence of local perception and description with scientific and
political term.
Survey Instrument Results
Evaluation of local knowledge on EcosystemServices
Yes 71%
Did not converge 59%
Do not know
5%
Converged6%
No 29%
Have you heard about ES? Cananeia(%)
Corumbataí(%)
Monteiro Lobato(%)
Yes 81 75 57
No 19 25 43
(%) Benefits from Ecosystem recognized by Locals
0
10
20
30
40
50
Cananeia (n=36)0
10
20
30
40
50
Corumbataí (n=31)
0
10
20
30
40
50
Monteiro Lobato(n=13)
Survey Instrument Results
1. Fish & sea food
2. Water flow and purification
3. Air quality & regulation
1. Water flow and purification
2. Local climate regulation
3. Air quality & regulation
1. Water flow and purification
2. Air quality & regulation
3. Local climate regulation
PC Eigenvalue % variance1 3.71525 65.5632 1.95142 34.437
Monteiro Lobato
Cananéia
Corumbataí
LoadingsPC 1 PC 2
B 0.29538 -0.068593 C -3.9171E-17
6.712E-18D 0.19074 0.31867E 0.19074 0.31867F 0.29538 -0.068593G 0.29538 -0.068593H 0.19074 0.31867I 0.29538 -0.068593J 0.29538 -0.068593K 0.19074 0.31867L 0.29538 -0.068593M 0.19074 0.31867N 0.29538 -0.068593O 0.29538 -0.068593P 0 0Q -0.10464 0.38726R -0.10464 0.38726S 0.10464 -0.38726T 0.29538 -0.068593
B-Recreational ; C-Aesthetics ; D-Cultural ; E-Natural heritage F-Bioquimica;G-Feira; H-Fibra; I-Madeira; J-Peixe; K-Agricultura; L-Agua; M-Biodiversidade;N-Polinizacao; O-Erosao; P-Carbono; Q-Clima global; R-Clima local; S-Purifica aguaT-qualidade do ar.
Survey Instrument Results : Principal Component Analysis
(%) Benefits from Ecosystem recognized by Locals
0
5
10
15
20
25
30
35
Cananeia (n=36) Corumbataí (n= 31) Monteiro Lobato (n=13)
Health
Education
Personal activities
Political voice and governance
Social connections
Environmental conditions
Personal insecurity
Economic insecurity
Feelings
Not Known inform
Survey Instrument Results: Wellbeing Evaluation
For categories of wellbeing we follow `The measurement of economic performance and social
progress revisited: Reflections and Overview` by Stiglitz et al 2014
1. Feelings
2. Social connections
3. Health
1. Feelings
2. Health and Economic Sec
3. Environmental Conditions
1. Environmental conditions
2. Health
3. Economic security
0
5
10
15
20
25
30
35
Cananeia (n=36)
Cananeia's producers
0
5
10
15
20
25
30
35
Corumbataí (n= 31)
Corumbatai's producers
0
5
10
15
20
25
30
35
Monteiro Lobato (n=13)
Monteiro Lobato's producers
Survey Instrument Results:
Wellbeing Evaluation in relation to Ecosystem Services
1. Water flow purify
2. Air regulation
3. Food
1. Others
2. Air regulation
3. Water flow purif
1. Others
2. Water flow purify
3. Air regulation
Linkages ES and Wellbeing
ES Perception Wellbeing Perception ES importance to
Wellbeing Perception
Cananeia 1. Fish/food
2. Water flow purif
3. Air regulation
1. Feelings
2. Social connections
3. Health
1. Water flow purify
2. Air regulation
3. Food
Corumbatai 1. Water flow purify
2. Air regulation
3. Local climate
1. Feelings
2. Health and
Economic Sec
3. Environmental
Conditions
1. Others
2. Air regulation
3. Water flow purif
Monteiro
Lobato
1. Water flow purify
2. Local climate
3. Air regulation
1. Environmental
conditions
2. Health
3. Economic security
1. Others
2. Water flow purify
3. Air regulation
Final Considerations of preliminary results
REGIONAL : Barometer of Sustainability
• The 3 regions are not sustainable
– in the agriculture commodities environmental performance were low (CO, ML) and high in
Protected Area (CA)
– Health Performance are low in Agriculture Commodities landscapes ; Socio-economic
dimensions showed relatively low in CA.
– LOCAL : INTERVIEWS
• In the 3 regions , people perceive ES as part of their wellbeing with central focus on water flow
/quality, air regulation , food , health, and environmental conditions.
• Provisioning services had priority in all 3 regions; Followed by Regulating Services (air quality
and local climate).
• The protected areas, people perceive two folds more ES than the other agriculture landscapes
(Corumbatai and Monteiro Lobato);
• In both agriculture dominated landscapes , water flow and air quality are the most cited ES.
• Health directly linked to Wellbeing in the 3 regions.
• Environmental Conditions linked to wellbeing in agricultural landscapes.
• In two regions (CA and CO) subjective wellbeing perception most important.
• In the 3 watersheds similar responses to ES perceptions and ES to wellbeing perception - > ES thus
intrinsically linked to wellbeing (?).
Science & Policy Relevance for Socio-Ecological Systems
- Local peoples perception matters
- Policy perception of local meaning matters
Scales Linkages
Climate Resilient Development
Sustainable development goals despite a
changing climate
Donors such as World Bank and
Europeans branches are aiming to
support climate resilient development - It
is beyond only material dimensions of
wellbeing as development program uses.
Examples of Policy Interventions based
on the integrated analysis :
Material dimensions of wellbeing (Access
transfer schemes such as PES , credit
line, etc -> improving capabilities via
providing conditions such as land reform,
market regulation)
Relational dimensions of wellbeing :
human and skills development schemes ;
empowerment via government reforms
Subjective dimensions : social and
cultural dimensions (SDGs)
Team & Funders
Pesquisadores Colaboradores
Internacional
University of Edinburgh
Dra Genevieve Patenaude
National
Dr Jean Pierre Ometto e Dr Peter M. Toledo – Centro de Ciência do Sistema Terrestre – INPE – São José dos Campos- SP
Dr. José Marengo – CEMADEN-São José dos Campos- SP
Dr. David Lapola, CEPAGRI-UNICAMP
Ms: Moara Canova, CEPAGRI, Unicamp
Ms: Adrían Gonzalez – IB- USP
Bianca Laurino –FFLCH
Giovana Marra –FFLCH – USP
Website: www.sabemclima.com.br