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Urban Futures: Leading Interdisciplinary Efforts in Urban Sustainability — Transcript

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0:00all right uh welcome for thanks for
0:03joining us again here today I'm Lawrence
0:05bja director of ral's climate science
0:07and applications program if you're in
0:10the audience today then you're probably
0:11already familiar with Patty Romero Lena
0:14and her Cutting Edge Urban Futures
0:16program but for those of you who are new
0:18to Patty well today you're in for a real
0:20treat uh Patty joined incar in 2006 and
0:23since then she's been a leading voice in
0:25expanding the scope of nar's
0:27interdisciplinary research areas
0:30collaborating with both enar and
0:32external scientists on highquality
0:35integrated research at the intersection
0:37of urbanization and environmental risk
0:40Patty tends to work at a very high level
0:42and has exercised strong scientific
0:44leadership on for many significant local
0:47National and international efforts uh
0:50these include such Global initiatives as
0:52future Earth UAC which is wcps
0:55urbanization and Global Environmental
0:57change project and the UN habitat
0:59program
1:00Patty was also a co-leading author for
1:02working group two of the Nobel prize
1:04winning ipcc fourth assessment report
1:07and was a convening lead author for the
1:08North American chapter of the more
1:10recent ipcc
1:12ar5 and yet she still takes the time uh
1:16to meet with and carefully explain the
1:18impacts of urbanization and climate
1:21change to the first graders in the
1:22elementary schools so whether it's
1:24participating in international
1:27negotiations uh like your presentation
1:29at the recent top 21 in Paris or working
1:32out one-on-one with residents in a slum
1:34in Mumbai India uh Patty brings a keen
1:37scientific curiosity and a deep
1:39theoretical background uh combined with
1:41an amazing level of passion and energy
1:43to her Cutting Edge Urban research this
1:46keeps inar at the scientific Forefront
1:48of this area so at this point I'm going
1:50to turn it over to Patty for her
1:52presentation there'll be a question and
1:53answer period at the end so I'll ask you
1:55to hold your questions to that time
1:58thank you um I'm glad that you already
2:01saved me some some minutes of my talk um
2:06well it is known for many of us H that
2:10cities are crucibles of
2:12innovative experiments and interventions
2:16seeking to reduce the impacts of flats
2:19and heat waves and to increase the
2:22capacities of populations to H mitigate
2:26and to respond to environmental change
2:28what is missing is are interdisciplinary
2:33efforts that help us understand how
2:35effective how sustainable how resilient
2:39those interventions are in that context
2:42the mission of urban Futures is to
2:44explore key intersections between
2:47urbanization and environmental change in
2:50order to inform action and World Views
2:53across scales in ways that Foster
2:57sustainable and livable cities and
2:59livable is very important believe me I
3:02am from a non liable city
3:06um we have focus our research in three
3:09research on three research fite the
3:12first tries to address how Urban
3:15Development impacts the environment the
3:18second tries to understand what are what
3:22factors determine vulnerabilities and
3:25resilience across and within cities and
3:28last but not least we also want to
3:32understand what factors what limits what
3:34barriers are out there to enhance
3:39decision makers and actors capacities to
3:41respond to these
3:43challenges um I won't be able to talk
3:45about all this three themes I will focus
3:48on the second
3:49one let me insist that something I have
3:53engaged in since I moved to Anar are
3:57research efforts that go from the global
3:59to the country to the city to the
4:02neighborhood and household level and
4:04backwards and I will tell you why a it's
4:09very important for social scientists to
4:11understand how the history of a city and
4:15his institutions which I will Define
4:17later on Define
4:19current ER patterns of vulnerability and
4:23risk but when I came to Anar I realized
4:27that we were faced with a a challenge
4:30similar to the one doctor's face a
4:33doctor is needs to be able to say
4:35whether someone is healthy or not we
4:39social scientists need to also be able
4:42to understand what factors May
4:45populations and cities vulnerable
4:47sustainable or resilient so our
4:50challenge is an upscaling challenge
4:53which is similar but different to the
4:56downscaling challenge many modelers here
5:00at Anar are faced with and I really have
5:02learned to respect what they do by the
5:04way
5:07um I will focus my presentation on five
5:10themes first I will share with you the
5:13rational the
5:14motivation uh for this work and our
5:18conceptual framing of risk then I will
5:21use three examples of our research to
5:25highlight how this framework has been
5:28tested the first focuses on the factors
5:33the determinance of urban populations
5:35vulnerability across and within cities
5:39the second focuses on unpacking
5:42urbanization because we all talk about
5:44urbanization but sometimes we don't
5:46measure it and we just say blah blah
5:49blah so what what are the links between
5:51urbanization and risk and this is an
5:53effort that we did focusing uh on global
5:57and uh the country scale
6:00and then I will go into a case study one
6:03of the most Recons we have been working
6:05with with h Joshua Sperling on a key
6:09dimension of Urban Development
6:11inequality as it relates with
6:13vulnerability of
6:15households I will use three these three
6:18examples to lay out my ideas my my
6:22suggestions for future research
6:25directions let me start with this first
6:28H with a motivation cities already face
6:32risk from climate relevant hazards this
6:35is a a map I did with Alex the shini for
6:40the UN habitat report on cities and
6:42climate change and what we measure here
6:45was the hazard risk of each City that
6:49represents a cumulative index based on
6:52the risk of impacts from exposure to
6:54four hazards Cyclones or or hurricanes
6:58flooding landslides and
7:01droughts we know from our work with
7:04Cynthia rosenb that and also from my
7:07work in the ipcc that human activities
7:11are expected to change the earth's
7:14climate in ways that can increase risks
7:17to
7:19cities I know also that it is very
7:22difficult to attribute many of the
7:24Dynamics of Hazards to climate
7:27change and therefore I'm because I have
7:30learned that by working within the ipcc
7:32and collaborating with my colleagues
7:34here at
7:36enar I am convinced that an integrated
7:39risk approach will be key to
7:42understanding and managing
7:44sustainability challenges in a changing
7:47climate equally important will be to
7:51understand the development context in
7:53which actors and decision makers make
7:56decisions if we don't do we will make
7:58many
8:00mistakes um how do we Define risks there
8:05are two big approaches to Urban risk for
8:08the first Tock down risk is the
8:11probability of a hazard occurrence
8:14multiplied by its consequences this is
8:16the most use here in
8:18Anar a modelers use scal down models to
8:23estimate future hazards such as Flats
8:27heat waves they explore adaptation
8:30Options under different climate and
8:32socioeconomic
8:34scenarios there is also what I would
8:37call the bottom up approach to risk this
8:40is more of a social science approach for
8:43which risk is the potential for
8:45uncertain outcomes where something of
8:48human value livelihoods lives property
8:52is at stake we need to keep that in mind
8:54because sometimes we think oh let's move
8:57that population from there well well
8:59that's not so easy it's it's quite
9:03complicated this approach uses
9:06vulnerability Frameworks that are
9:08similar to the models that we use in the
9:12top down approach it combines
9:15quantitative and qualitative methods and
9:17data what is very important it helps us
9:21understand the culture the history the
9:24institutions of a city and it combines
9:26that with quantitative methods and I
9:28will refer to that to examples of how
9:31this is
9:33done a the the framework we suggest
9:38integrates both approaches and let me
9:41just ask you to remember that the top-
9:44down approach tends to focus on the
9:47Dynamics of environmental change as they
9:50affect Hazard exposure while the second
9:54approach tends to focus on the societal
9:57factors governance one of the
10:00that explain differences in access to
10:04assets and options to respond to hazards
10:07in this approach which which Builds on
10:10the ipcc reports the one on risks and
10:14the number five a hazards are
10:20stresses such as flats and social unrest
10:23because people do not only deal with the
10:26environment a people economic social and
10:29infrastructural assets are exposed to
10:32the final impacts of exposure to this
10:35depends on societal factors and
10:38ecological factors and those are defined
10:42with the concept of vulnerability which
10:45is the propensity to be adversely
10:48affected the contrary of it is
10:51resilience the capacity to perceive risk
10:54and effectively
10:57adapt I want also to insist that the
11:01exposed units can also be sensitive and
11:04that we Define as a
11:06degree by which they can be negatively
11:09or positively
11:11affected the capacity of H soci
11:14ecological systems and of populations
11:16which I will focus on is a pull of
11:19assets such as
11:21education information and social
11:24networks actors can use to manage risk
11:28while at attending their development
11:31needs actors who are those actors um not
11:35Angelina Jolie but actors are the
11:37government the private sector NOS
11:40scientists and the
11:42media these assets and options are
11:45unequally distributed and therefore we
11:48use the concept of social
11:50inequality defined as a condition that
11:53ansers when assets are distributed
11:57unevenly these actors do not not work
12:00and operate in a vacuum they operate in
12:04what we call governance and we Define as
12:06the set of formal and informal I want to
12:09insist informal rules because I will
12:11come back to that rule making systems
12:14such as laws and regulations actor
12:18networks at all levels to steer cities
12:21towards or away from
12:24sustainability with this framing let me
12:27now show you how we have been testing it
12:30and um what our findings are and let me
12:34start by the first example Urban
12:36population vulnerability that's
12:38something I did with Katy Dickinson who
12:40is there and with
12:44hin we are aware that context matters
12:49but we hypothesized in this project that
12:52was awarded by NSF that it is possible
12:55to identify patterns of populations
12:58vulnerability across Urban centers and
13:01what is equally important research
13:03approaches we develop a metaanalysis and
13:07meta knowledge or sociology of science
13:09I'm a sociology so that's why I did it
13:11right approach and we focus on
13:15temperature related hazards such as heat
13:17waves why did we focus on those because
13:20they are associated to large
13:23impacts because they are clearly tied to
13:27climate change we were able to assess a
13:31large number of
13:32studies and to cover 224
13:37cities so okay what is urban population
13:40vulnerability although everyone defines
13:43vulnerability the way I already share
13:45with you not everyone measures
13:48vulnerability equally we identify three
13:52approaches to vulnerability for the top
13:56down apply by 80% 88 % of the papers and
14:01which is mostly used by epidemiologist
14:03climate modelers and some natural
14:05hazards
14:07communities a vulnerability results from
14:10exposure to a hazard and they measure it
14:14by exploring quantifying the temperature
14:17Health outcome and a a a also
14:22quantifying confounding factors such as
14:25age and such as education let me ring
14:28some water
14:31that's what these things do to you
14:34huh okay for the second
14:38approach Al also
14:41remember I I'm mapping my framework and
14:44you are seeing how I'm going from
14:45Concepts to methods to data to results
14:49and backwards so for the second approach
14:52applied by 11% of the papers and which
14:55is mostly a social science
14:57approach um
14:59vulnerability is defined by differences
15:03in capacities that are driven by what we
15:07call structural factors such as
15:10inequality governance and
15:13organization while the topown approach
15:15focuses on the individual without
15:18considering the social
15:20environment the social science approach
15:23focuses on
15:24social dynamics and sometimes forgets
15:27about the environmental conditions
15:31therefore many scholars particularly
15:33some of us at cisa have developed
15:36integrated approaches that integrate
15:38both
15:40a approaches to risk and that focus on
15:45both the
15:46socioecological and social and
15:49ecological determinants and also the
15:51underlying drivers of
15:53vulnerability sadly this approach was
15:56only applied by 11% of the P papers we
16:01analyze we also applied the ipcc
16:04approach to assessing and certainty and
16:08what we did was
16:11to H quantify the
16:14evidence meaning the number of papers
16:18identifying a factor as determinant of
16:20population vulnerability and the
16:22agreement among
16:25Scholars and how did we measure this it
16:29was hard right Kathy it was not an easy
16:32process but we did it and what we did
16:35was
16:36to identify those Hazard
16:39indicators such as the timing of a heat
16:42wve the levels of temperature and what
16:44is very important the thresholds which
16:46we call temperature magnitude we also
16:50use indicators of exposure so such as
16:53population density total population
16:55vegetation and indicators of capacity so
16:58such as access to education social
17:02networks and H we also use asign
17:07symbology to uh define whether the
17:10indicator is positively related to
17:13vulnerability meaning increases it or
17:16negatively meaning decreases it or there
17:21there is no agreement H such as the the
17:26relationship is a no relationship
17:30we found that 13 factors which are
17:33mostly located in these quadrants
17:36account for 60% of the
17:40tales and we also found that only two
17:43determinants two of the many
17:45determinants that play a role in
17:47defining
17:48vulnerability are analyzed by studies
17:51age and temperature
17:53magnitude these findings result from the
17:56dominance of the top- down approach I
17:59mean we need it but it is not enough
18:01that is our point we also found and we
18:04ma the cities covered in the studies and
18:08we found no surprise that most of the
18:11studies focus on the US and and
18:15Europe well we have found in in our ipcc
18:19reports that most of the
18:21vulnerability tends to be located
18:24here in
18:26summary we found that it is possible to
18:29identify patterns of vulnerability
18:31across
18:33cities uh and research appro and and
18:37also to make the city the the approaches
18:40the research approaches comparable which
18:43is a huge challenge for scientific
18:47progress to happen we also find found
18:50that knowledge has examined only certain
18:54aspects and that scale has been key in
18:56defining also why some aspects are
19:00neglected and this was the first time I
19:02was confronted with uncertainty Linda I
19:04couldn't believe it uncertainty given by
19:07the approach used that focuses on some
19:10things forgets others the methods and
19:13data and the scale of analysis we were
19:16able also to justify why an integrated
19:19approach is needed let me now move to
19:22the second example the organization
19:25Dynamics shaping risk
19:28our
19:30hypothesis states that a focus on only
19:34the urbanization exposure interactions
19:37is not enough to understand Urban
19:40risk first of all we need to really
19:43unpack
19:44urbanization second we also need to
19:47include in indicators of sensitivity and
19:50capacity what we did and I did this with
19:53a colleague a very young and promising
19:56scholar from Germany a g
19:59what we did was to use two indicators of
20:04urban urban levels or urbanization
20:07levels and er growth economic growth and
20:11two indicators of the rate of
20:13urbanization and economic growth we
20:16applied a hierarchical clustering anal
20:19cluster analysis to group countries in
20:2110 groups and we correlated
20:25these um numbers right these indicators
20:28with indicators of exposure such as
20:32populations in presence and in contact
20:34with affected by storms floods sea level
20:37rise and droughts sensitivity such as
20:41population
20:43undernourished dependency ratios and
20:46poverty indicators and indicators of
20:49lack of adaptive capacity such as the
20:54corruption perception index which in our
20:56countries um is very important you well
20:59also in the US it's very important
21:01access to Medical Services gender
21:04Equity access to
21:07education and quality of ecosystems we
21:12also use indicators around all all these
21:14and you will see that these indicators
21:15appear over and over in in our analysis
21:19what is what we
21:20found well let me just give you some
21:23highlights for sure we were able to
21:25create the oecd group which registers
21:29high levels of urbanization low levels
21:31of urban growth high levels of GDP per
21:35capita or GN per capita a low levels of
21:39economic growth we also have a typical
21:42case represented by southeast
21:45Asia with high levels of urbanization I
21:49I mean I think that China and India are
21:52on steroids there is no other way I can
21:55describe it for good and for not so good
21:57like
21:59so so again high levels of urban
22:03growth in this case there are high
22:06levels of urbanization but low levels of
22:08economic growth which really I mean
22:11these are indicators that that really
22:14get at at one of the two of the key
22:17elements of
22:18urbanization so we also found that
22:22rather than exposure sensitivity and
22:25capacity indicators contribute to
22:28overall risk and let me just show
22:31you how is it that we could test H prove
22:35this these are plots where we have here
22:38the in on the Y ax the index values and
22:41on the X the country groups and we found
22:45that in terms of exposure there's not so
22:49much difference between country groups
22:52that's not the case once we include
22:55indicators of sensitivity and lack of ad
22:58the capacity once we include those which
23:01are indicators of development or lack of
23:05once we include those then this group
23:08this group and this group are
23:10particularly
23:12vulnerable we also found that rather
23:15than organization
23:17levels it is a race of urbanization that
23:20influence
23:21risk of
23:23course I and this is what I summarize
23:26here right again rate of urbanization
23:29is more important is a a key driver of
23:33risk sensitivity and capacity indicators
23:36contribute more to over overall risk but
23:40let me tell you this is just the
23:42beginning of the
23:44conversation why because Urban
23:45Development is more than economics and
23:48more than demographics it's also buil
23:50environment characteristics is also
23:53governance and is also Equity so there
23:56is a lot of stuff to to to
23:59do and and I will give an example of how
24:03we address the links between social
24:06inequality and
24:09vulnerability in the city of Mumbai this
24:12is part of a an NSF P award I engage
24:17with and I'm really happy I did with um
24:21Jos Sperling I really learned a lot from
24:24you and by being with you Josh and I
24:26hope we stay together so
24:29um our hypothesis is similar to the
24:32prior one under current conditions
24:35current climate conditions poverty and
24:38capacity contribute largely to overall
24:42vulnerability why mbai India well I like
24:47large cities I guess no not not only
24:50that I mean Mumbai is one of the largest
24:5410 largest cities depending on the
24:57boundary we use to define the city it
25:00has between 18 and 22 million
25:03people it has high levels of risks of
25:07impacts from sea level rise Storm surges
25:10and flooding and that's why we did our
25:12field War during the flood during the
25:14monsoon and this risk results not only
25:19from
25:21hydrometeorological conditions that are
25:22changing with climate change it also
25:25results from Human Action a key element
25:28here is a huge Reclamation of large
25:31areas of what used to be seven islands
25:34of land just above sea level with many
25:38of these areas below the high TI level
25:41and just now we are mapping this this
25:45but I cannot talk about that just now so
25:48besides that the city is faced with high
25:51levels of inequality I am from Mexico
25:54City I'm used to deal with poverty I
25:57grew up in a poor neighborhood and I
26:00used to engage with how to address
26:02poverty but once I moved I started to
26:05work in Mumbai I say okay guys we need
26:08to address this issue the levels and
26:10qualities of poverty in Mumbai are
26:15special some data some indicators 70% of
26:19the population engages in the informal
26:22economy defined by the government H by
26:25as nonconforming with the regulations
26:29one out of four children is stuned an
26:33informality remember the formal and
26:36informal rules informality is a key
26:40institutional component
26:42defining social inequality in many ways
26:46informality is a state of regul
26:49regulatory flux and I know it from being
26:52with my mom when she was selling on the
26:55streets I mean where the legal and the
26:57illegal Al are up for negotiation
27:01contestation and
27:04Corruption and this informality has
27:06implications for for vulnerability as
27:09well because by not having access to
27:13secure land tenure many of these po
27:16populations are
27:18criminalized and do not have access to
27:21many sources of capacity such as
27:24infrastructures and
27:25services a key other finding and this is
27:28for kaspar who is not here kaspar Amon
27:32and I will share that we will see
27:34whether we can do something together
27:36although kaspar is already finding that
27:40heat waves will be a key Hazard facing
27:43City such as Mumbai the levels of
27:46penetration of air conditioning are very
27:48low of between 8% and
27:5212% equally important is the fact that
27:56the study the the analysis of the
27:59influence of wealth and vulnerability
28:02indicators offers a lot of
28:04methodological challenges not only
28:06because these are multi-dimensional
28:08Concepts I have been talking a lot about
28:10vulnerability but let me tell
28:12you poverty which is one expressions of
28:16expression of social inequality can be
28:18measured by income indicators expend
28:21expenditure indicators and assets
28:23indicators and each approach offers very
28:27different
28:29results um and I also found that again
28:32for you it's a present to you Linda that
28:35and certainty is a key challenge when
28:37dealing with this
28:41why to what is this challenge related to
28:44indicator selection to index
28:47construction and to an understanding an
28:50accurate understanding of the influence
28:52of indicators on on the outcome we want
28:54to explore and I want to give you here
28:56an example
28:59and I I I will use an
29:01image to study vulnerability we usually
29:05use the same ingredients more or less
29:07the same ingredients which are our
29:09indicators but the characteristics of
29:11the indicators vary across context or
29:14which
29:16context in Latin American cities such as
29:19Mexico
29:21uh access to improved toilet facilities
29:24is of the essence and it is mostly
29:27related to connection to the SE system
29:30in India Indian cities such as Mumbai
29:34besides
29:35that a key challenge is given by the
29:38fact that between 40 and 80
29:41households share these
29:44toilets that's that's I mean they justed
29:46to tell me but you are talking about
29:48climate change guess what this is our
29:50challenge how do we deal with
29:53that okay how did we address that well
29:57uh
29:58we develop again our indicators and
30:02um you I already have talk about
30:05indicators of exposure sensitivity and
30:07capacity I just want to say that we
30:10capture some indicators of Hazard impact
30:13such as the number of Hazards people
30:17experience households experience and
30:20whether they suffer a health impact from
30:24exposure to those I just want to insist
30:27that
30:28um we added these
30:31indicators which are access to material
30:34possessions and access to physical
30:37assets which in or Urban centers is key
30:40right and what we did was to combine
30:43qualitative methods meaning Knowledge
30:47from local experts with quantitative
30:49methods which allow us to embrace the
30:52subjectivity involved in Waiting
30:55indicators and to build and and we
30:58combine that with the FY logic to build
31:01a four household
31:05classes a the low moderate high and very
31:10high vulnerability class the first
31:13finding is not surprising 55% of our
31:17households are highly
31:19vulnerable and 28% are very highly
31:23vulnerable the two groups make 2/3 of
31:26the population that's not surprising but
31:28what is
31:30surprising is
31:32that a with the exception of the very
31:36high vulnerability class which is faced
31:39with very high levels of
31:44exposure the other groups the other
31:46household classes are faced with similar
31:50levels of exposure and what defines
31:53vulnerability are differences in poverty
31:56which increase
31:59yeah when we go from the low to the very
32:03high vulnerability class and with
32:05capacity indicators which decrease when
32:08we go from the low vulnerability to the
32:11very high vulnerability
32:14class let me just really tell you a
32:16little bit more about the capacity
32:18indicators which really
32:20Define not not only access to assets but
32:24also agency which for us social
32:26scientists is so key because people do
32:29not only receive hazards and say oh let
32:32me go and be killed no people are active
32:35and they they really are very creative
32:38okay so we found that in terms of
32:41awareness and priority given to
32:44mitigation risk mitigation and
32:46adaptation
32:47policies our household classes are more
32:50or less
32:51similar but we also found that and this
32:55is a finding that is similar to what we
32:57found in the other for Latin American
32:58cities I have
33:02studied while the very high
33:04vulnerability class is very likely to
33:08rely in family support and neighborhoods
33:10to respond to
33:12emergencies the other classes are very
33:16highly likely to use social networks
33:19such as Nos and religious
33:23groups well the education assets are
33:26really related to class to so soci
33:29economic status not surprised but
33:31interesting and this is important for us
33:33at
33:35Anar the with the exception of our very
33:39high vulnerability class the other
33:41classes are very likely to use warning
33:44sources such as TV and radio to inform
33:48themselves about risks this
33:52is a very challenging table and I'll see
33:57whether
33:58you like it this time um
34:02so there's so much conversation about
34:05thresholds or points after
34:07which a heat waves move from some
34:11conditions to the others right what we
34:14did with this figure which is only one
34:16example of three we are
34:18working with what this figure shows are
34:22threshold points for capacity and
34:25exposure as we move from
34:29high to low
34:31capacity a point or some points are
34:34Reach In which levels of vulnerability
34:37start to ramp up we move from one regime
34:41to another regime likewise when we move
34:45from low exposure to high exposure
34:49levels in each case a a level is
34:53reach H at which further increases do
34:57result in high higher
35:00vulnerability um
35:02regimes but what is interesting and this
35:05is really bringing me back to one
35:07hypothesis I I love so much which is the
35:10the idea that also the rich will be at
35:12risk and why is that important because I
35:15have seen that historically only when
35:17the wealthy are affected they take care
35:19of things so there is also a point at
35:24which and this is our next regime there
35:28is also a point at which increases in
35:29exposure affect populations equally
35:32across capacity
35:35levels to summarize under current
35:38climate conditions poverty and capacity
35:41indicators contribute largely to
35:44households overall
35:46vulnerability another important
35:48conclusion is that these methods which
35:50are really a combination of qualitative
35:52and quantitative methods are key and
35:56complement what we do here enar in that
35:58they capture the multi-dimensionality
36:01and uncertainty in vulnerability and
36:03risk analysis help us evaluate the
36:06contribution of wealth sensitivity and
36:09capacity indicators and what is very
36:12important they help they support policy
36:14interventions that Target attributes
36:17under different combinations across
36:20household classes let me close with some
36:25conclusions and some consideration of
36:28what could be done with this area of
36:33research I'm convinced that both
36:35approaches are needed both approaches to
36:38risk are needed one sheds lights on some
36:42aspects but is not enough either one
36:46even if it is social science or physical
36:47sciences that are coming together to
36:49address this issue we need to combine
36:52qualitative and quantitative methods
36:55only by being and doing fre work and
36:58interviewing people and listening to
37:00them and going to meetings and going to
37:02meetings and going to meetings we get
37:04the trust we need to really get to
37:07understand the role of power the RO the
37:09role of governance and how people really
37:12feel whether their interests are taken
37:15care of I mean I have some quotes of how
37:18people from lowincome areas in
37:20buenosaires Mexico City Santiago and and
37:24and bota are aware of the fact that
37:26without
37:28governance without governmental support
37:30they won't be able to make
37:32it equally important is the fact that
37:35and this is something uh I I I will
37:38include in another paper addressing some
37:41other data about Mumbai the economics of
37:44this are key and why are they key
37:47because you need also an economic
37:49threshold in terms of levels of a a
37:52income City authorities need to invest
37:56in things
37:58and when you have high levels of
38:00informality you don't get that those
38:02sources of income and that you only
38:04understand if you go to to the places
38:06and listen to people and you combine all
38:09these with quantitative methods and um
38:12and also you you you learn to be humble
38:14about things right so I hope I was able
38:18to show how multiple scales bring shed
38:22light on different components of
38:26vulnerability and resilience
38:28and also how some patterns cut across
38:31scales right that's interesting I was
38:33like wow I cannot believe it huh wealth
38:35and capacity indicators contribute at
38:38least in in in in in Mumbai I think also
38:41in other cities contribute more to
38:42overall risk and vulnerability than
38:45exposure
38:46indicators it's true that that might
38:48change and it's already starting to
38:51change and a changing
38:54climate before I go to the Future
38:57directions I want to insist that I only
38:59present our work on this theme It Is by
39:04working on the three themes that I have
39:05been able to find other connections and
39:08I will refer to those um what can I say
39:12about future
39:14directions first of all it's really
39:17important to
39:18unpack the key dimensions of Orban
39:21development we already have some work
39:24dealing with demographics and economics
39:27we need to also deal with governance
39:29indicators with buil environment
39:31indicators such as
39:33infrastructure the quality of housing
39:36for instance in in in um Indian cities
39:40having a separate cooking space is key
39:43that's not the case in in Latin American
39:45cities we we we already have that so and
39:50we also H
39:52need a social indicators such as
39:55inequality indicators and I think that
39:56in this context where everyone around
39:59the world is starting to say enough
40:01inequality has increased we need to also
40:04address issues of
40:06inequality
40:08er a another important point is the
40:12issue of thresholds and tipping points
40:14and and let me tell you a threshold is
40:18defined as a point at which one Rel
40:20relatively stable so eological
40:23H and governance regime gives what way
40:27to
40:28another what is interesting is that
40:31while sociological thresholds are
40:33contingent upon the interaction among
40:35physical hydroecological and social
40:38processes governance thresholds are an
40:42integral to the way societies work which
40:45limits contingent upon ethics meaning
40:50values politics knowledge and culture
40:53and politics meaning power so
40:59I want to move forward and I'm working
41:02on that with a colleagues from the
41:05future Earth Urban
41:08platform U which is transitioning into
41:11future
41:12Earth and what we want to
41:15do I know it's crazy but what we want to
41:18do is to connect the problem space with
41:22the solution space and what I mean by
41:25that is that we social scientists
41:28physical scientists engineers tend to
41:31always focus on okay how urbanization
41:34comes together with haard and
41:36institutions to drive risk and
41:38resilience that's nice but once we come
41:41to decision makers they say well what do
41:43I do with all this information well we
41:46also need to understand how people make
41:48decisions and I see ourselves inerting
41:51our groups in this solution space but
41:55what we need to do is to understand what
41:57whether there is a fit or lack of fit I
42:00think there is a lack of fit between the
42:02two
42:03domains and we are studying that in in
42:06two projects one is called unicorn
42:09urbanization water management and flood
42:11Race Across mountain regions I'm working
42:14with Andy monagan on that and we are
42:16waiting to see what we will whether the
42:19Belmont Forum will U award our proposal
42:23and with
42:24unmask uh a proposal on urbanization
42:28food energy water systems and extreme
42:30hazard risks that we will submit and so
42:35besides working on this H well I I'm
42:39working with siia which help me on and
42:42we try to understand uncertainty noral
42:44vulnerability and risk um Linda MS is
42:48our Mentor in this so I mean these are
42:52again just some examples of future
42:55directions I wouldn't like to finish
43:00without saying that my ideas wouldn't be
43:05possible without the
43:08beautiful projects and collaborations
43:10and partners I'm engaged with in Anar at
43:14Anar they are with
43:16red um in the
43:19US and globally without them I wouldn't
43:22be able to be challenged particularly if
43:25engineers and physical scientists
43:26challeng me and I want to kill them but
43:29next day I say I need to see how to do
43:32this they are right and let me get it
43:35okay so I also want to really thank my
43:39posts I mean I call them mine but they
43:41are not mine um because they also have
43:44been a source of inspiration for me they
43:47always have good ideas they come with
43:49interesting stuff and um I also want to
43:52thank them for for for being part of
43:54this I also want to insist that
43:58many components of what I do are the
44:02result of awards but others are the
44:04result of just sitting together after
44:06meetings and saying hey I listen to what
44:09you say why don't we do this for
44:10instance the survey we conducted in in
44:13Mumbai was the result of a collaboration
44:15with Indian Partners who were conducting
44:18an atmospher in campaign field campaign
44:21they had 90 students they said if you
44:23train my students we conduct the surveys
44:26I said voila I'll do it so there is a
44:29combination of many things involved here
44:32and again I want to thank them and thank
44:34you for taking the time to be here with
44:37me
44:39[Applause]
44:45y all right so Pat I'll let you uh
44:48choose who you who you want to answer
44:50the questions from so we'll open it up
44:51to questions from the audience
45:04hi I'm Daniel uh from atmosphere
45:06chemistry um very inspiring talk I I
45:09really like the this the slide about the
45:13um moving the solution space into the
45:15problem space or more connections do you
45:17have any um examples of what that would
45:19look like uh specifically or any um
45:23situations where you've seen a shift
45:26happen
45:28right in uh my work on governance I
45:31learned that there are always Misfits in
45:35terms of the scales the spatial and
45:38temporal scales at which a vulnerability
45:41and risk operate and the temporal and
45:44special scales at which decision makers
45:47operate I give you an example for water
45:50management you use the basing I mean as
45:53a researcher right well decision makers
45:56have made I mean there are some
45:58exceptions to that but usually they have
46:01many
46:03jurisdictions coming together to deal
46:05with that Basin and they overlap they
46:09are fragmented Etc that's a problem of
46:11fit another problem of fit is given by
46:14the fact that usually decision makers
46:16work at a a temporal scale that goes
46:19from I mean if you are lucky 10 10 years
46:22are the maximum well many processes we
46:25are dealing with here
46:27our 50 years process processes for
46:30instance just now even if we were able
46:33to mitigate to to introduce mitigation
46:37policies and move us to a trajectory a
46:404.5 trajectory right in the right
46:43whereby we could reduce uh bring our
46:47temperature to 1.5 to2 even if we were
46:49able to do that the effects of what we
46:52will do will be manifest in 50 years
46:55many decisions decision tell me py I
46:58want I'll be here for 3 years what are
47:00you talking about so those are two
47:02examples but now under a changing
47:05climate there are two other issues of a
47:08pit that I want to work with one is
47:11thresholds meaning are we
47:14really identifying thresholds or
47:17not and cascading effects and what
47:21happened with the flats in 2013 here in
47:24bould is an example of that I mean the
47:27the the the impacts of the flats were
47:29not only the result of the the huge
47:31amount of precipitation we receive but
47:35it was those were also the result of
47:37some areas some ecosystem areas not
47:41working as multiple use areas such as
47:46the the the walking and and biking
47:49spaces we have invol there that during
47:52Flats can also help you mitigate the
47:55impact of flats that together with how
47:58people act and operate creates a series
48:02of effects that we call cascading
48:04effects that perhaps we are not ready to
48:06deal with so what I consider is that we
48:08need to add those two to our efforts to
48:12support decision makers and to engage
48:15with them and learn from them I'm sure
48:17they will talk about more but I I really
48:19think we need to bring those spaces
48:22together to really connect the dos
48:30yeah yeah
48:37okay so uh there's a lot of unauthorized
48:40Construction in some of these cities how
48:42do you get data and things like that for
48:45for what again unauthorized
48:47constructions well you know uh there are
48:49some methods one is a participatory GIS
48:53approach whereby you go with people I
48:56mean before the gis we did it by
49:00working is problem it's in general a
49:03problem so I mean you can use
49:06participatory GIS now but when I used to
49:09do it we just work with people and we we
49:11we really H SW sweep around around
49:15places and they started to say look this
49:17area is not acknowledged but we have so
49:19many houses here Etc so there are many
49:22approaches and now again the gis is
49:24really making us do things but what is
49:27important is you might have all this
49:29information if decision makers don't
49:32want to listen to you believe me they
49:34won't listen to you so the challenge is
49:37how you frame issues in such a way that
49:40you can engage with them and that's not
49:43a challenge of us telling you know
49:45decision maker you are not getting it
49:47because they will say you know what go
49:49away I cannot you know you really need
49:52to for instance with Josh we we are just
49:55now analyzing our interviews in in
49:57Mumbai we were looking for climate
49:59change policies and we found development
50:01policies that are driving
50:04risk
50:06so
50:12back vi from uh
50:14U uh next yeah here before that yeah
50:19okay in the fourth one you say wealth
50:21and capacity indicator contribute more
50:24than exposure indicators could you
50:26differentiate between those two what
50:28exposure indicators you meant are they
50:30both connected somehow right I will
50:33um let me give you examples here
50:38a capacity indicators
50:41include such factors such as education
50:45we have found that people who are
50:46educated are more a more able to look
50:50for sources of information to respond to
50:52challenges and
50:54Hazards and also ask access to
50:57governmental support in general and
51:01during emergencies and access to family
51:04members to community Grassroots to H
51:09Church
51:10organizations ER
51:12which are elements of socio
51:15institutional capacity and last but and
51:19equally important are also indicators of
51:22information how people perceive risks
51:25and whether they use the TV the internet
51:28or not to respond to hazards to to to to
51:32to respond to early warning systems are
51:35key elements defining vulnerability and
51:38then in terms of exposure and
51:41sensitivity a people wouldn't be
51:45vulnerable if they were not not exposed
51:48to something you know you need to be
51:50exposed to a heatwave to a flat to be
51:53vulnerable okay so we measure that
51:57by asking
51:58people to which H heat waves air
52:02pollution and other hazards they have
52:03been exposed and whether they can
52:05identify impact levels and health
52:09outcomes from that exposure and we have
52:13found in the literature and in our prior
52:15work that the elderly for instance are
52:17more sensitive to heat waves and that um
52:21women are more sensitive to Flats in
52:25some areas particularly because they
52:27cannot go out they need to to I mean
52:29there are cultural issues that constrain
52:32them so and prexisting medical
52:34conditions if you already have a hard
52:37condition it will be very hard for you
52:39to deal with a heatwave so those are the
52:43exposure capacity and wealth indicators
52:46we use so what we
52:49found again is that in terms of exposure
52:53which we have here these are our index
52:55values average index values and we have
52:59levels of uncertainty by the way ER in
53:02terms of that many of the households we
53:06interview are more or less equally
53:08exposed with the exception of the very
53:11highly vulnerable household that's not
53:13the case when you include indicators of
53:16capacity the more vulnerable you
53:19are the less capacity you have remember
53:23those that we use and then the poorer
53:26you are the more vulnerable you are so
53:29that's that's why we concluded that
53:31thank
53:33you
53:40MH Marcus mench um have you thought of
53:44uh taking and adding into your kind of
53:47re key research issues the question of
53:51surprise because I think in terms of
53:54thresholds and uh both the Cascades
53:58where the relationship between kind of
54:01wealth capacity might come apart is when
54:04you have things that are outside and the
54:07boulder floods might be an example of
54:08that on a small scale right yes and
54:11that's why as you can see here
54:14independently of Your Capacity levels
54:17once you reach the point8 level of
54:20exposure then you you are at risk
54:27so
54:29um this is something that I already have
54:32been talking about when dealing with
54:34exposure to air pollution and H
54:37particularly atmospheric modelers really
54:39pay a lot of attention to this and in
54:42our analysis of thresholds and cascading
54:45effects we are including the issue of
54:47surprise but again this is a project we
54:49are to to start so yeah we are including
54:53that but that's a very important H
54:55comment
55:01so following on does that um go into the
55:04exposure index then the
55:06surprise yes you know and yes it is here
55:10and you know it it was interesting in in
55:13three cities of Latin America
55:15Santiago bota and Mexico we run some Pon
55:20regressions to ex to examine the links
55:23between exposure to a pollution and
55:26Health outcomes we use mortality
55:28indicators which are extreme by the way
55:30that's a caveat with that and then we
55:32correlate that with special data
55:34vulnerab on vulnerability indicators and
55:37we found
55:38that a exposure I mean impacts from
55:41exposure to air
55:43pollution in these
55:46cities cut across a socioeconomic status
55:50only air pollution and I'm only talking
55:53about extreme impacts um we are also
55:56aware of the fact that these H wealthier
55:59households have a lot of assets to
56:01respond but still I mean I think that we
56:05need to start pointing to the fact that
56:08wealthier populations are not
56:19spared all right thank you for joining
56:23again once thank you oh Linda sorry
56:27you Blended I blended in as usual um on
56:31Linda MS from enar um it's a probably a
56:36tricky question to answer this late in
56:38the time but I wonder if you could
56:40succinctly uh I think the audience would
56:42probably be rather interested in some of
56:45um your activities at cop 21 in terms of
56:49the side event of cities if there's a
56:51few pearls that you could throw before
56:55the audience yes I I work together with
56:58Cynthia roseni and we put together a
57:01synthesis book a report on cities and
57:04climate change and what we did was to
57:06engage with stakeholders and to really
57:09uh uh also made them part of all these
57:11synthesis efforts where we did not only
57:15explore what will happen with climate
57:17hazards but also how authorities are
57:20responding to these problems in the
57:23water Health uh build environment I mean
57:26in many sectors but what was interesting
57:29was that um we really or is that we have
57:33been engaged with the the Covenant of
57:35Mayors and with the eay and with key
57:39authorities that are really addressing
57:41these issues at the global level but is
57:43also nice is that I am involved with the
57:46city of Boulder uh I'm going to their
57:49meetings learning a lot from them and we
57:52are having a very nice dialogue about
57:54the cup so there are many things that
57:57are going on and I'm very excited about
57:59them and and and what we could do here
58:02at Anar to inform those
58:08decisions all right with that I think
58:11we'll close today again thank you Patty
58:13and thank you for

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