Lankao csu october 2013


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  • Total emissions too. The order changes dramatically
  • Talk about methods
  • Talk about methods
  • Talk about methods
  • Talk about methods
  • Lankao csu october 2013

    1. 1. How to enhance cities' role in transitioning to sustainability and resilience? Urban Futures NCAR Paty Romero-Lankao, Sara Hughes, Dan Runfola, Josh Sperling
    2. 2. Urban Futures Special Issue on Cities and Climate Change 1. Dynamics of urbanization that shape urban emissions, vulnerability and risk 2. Cities’ institutional capacity to meet the challenges of reducing emissions while improving resilience CLA for AR4 and AR5 Background report and chapters 1, 2 and 7
    3. 3. Urban mitigation challenges: The largest cities don't necessarily have the largest carbon footprints. Why? Total carbon emissions by city 15.2 M. of people 250 200 19.7 M. of people 150 100 M of tonnes of CO2 e. 50 Source: Romero Lankao (2008) Los Angeles Tokyo New York City Shanghai Beijing Mexico City London Seoul Toronto Stokholm Johannesburg Cape Town Durban São Paulo Delhi Austin Rio de Janeiro District of Columbia Barcelona Kolkata N. Mandela San Diego Dhaka Oxford Chiang Mai Baguio 0
    4. 4. Austin District of Columbia Los Angeles San Diego Toronto Shanghai New York City Beijing Oxford Cape Town London Durban Johannesburg Tokyo N. Mandela Stokholm Seoul Mexico City Barcelona Rio de Janeiro Dhaka Delhi São Paulo Kolkata Chiang Mai Baguio Carbon emissions per capita 25 20 15 10 5 tonnes of CO2 equivalent 0
    5. 5. Multiple factors differences in urban GHG emissions Monocentric Poly-centric  Economic base and GDP per capita  Urban form and population density (1% increase in urban density results in a 1.25% decrease in emissions) Sources: Romero Lankao, Tribbia and Nychka (2009); Bertaud (2009) Database by Kenworthy covering 84 cities STIRPAT formula (instead of multiplicative IPAT)  Estimates elasticity of each driver  Energy use intensity (public utility key here)  Transportation mode share 1% increase in public transport results in 0.15% decrease in emissions!  City’s latitude & energy endowments
    6. 6. Cities face greatest risks from climate change Contoured: hazard risk associated with climate change De Sherbinin and Romero Lankao (2008): The hazard risk of each city represents a cumulative score based on risk of cyclones, flooding, landslides and drought
    7. 7. Factors shaping urban populations’ vulnerability to temperature-hazards 1. Many case studies, different a. Hazards (focus: temperature) b. Urban areas c. Dimensions d. Framework: Urban vulnerability and risk Research Paradigms 2. Validation of conceptual framework 3. Mixed methods a. 4. meta-analysis & metaknowledge 53 papers covering 224 cities Romero-Lankao and Qin (2011, COSUST) Romero Lankao, Qin and Dickinson (2012, GEC)
    8. 8. Determinants of urban vulnerability: evidence and agreement - 13 factors account for 66% of tallies on determinants of urban populations’ vulnerability - 2 determinants extensively studied: hazard magnitude and age (1) Text color denotes categories of vulnerability dimensions. Green = Hazard; Yellow = Exposure; Red = Sensitivity; Blue = Adaptive capacity/adaptation (2) Symbols in parentheses = direction of relationship between vulnerability and outcome (medium or high level of agreement only) + positive relationship (increases vulnerability); - negative relationship (decreases vulnerability); ~ no relationship - Findings result from dominance of a paradigm “urban vulnerability as impact”
    9. 9. Dynamics of urbanization shaping vulnerability (global level) 1. Not only levels but also rates of urbanization influence vulnerability 8 Country groups 10 2. Not only exposure but also sensitivity and capacity  Urbanization and economic indicators to cluster countries  6 Cross-correlation of clusters with national-level normalized subindices of hazard exposure, sensitivity, and adaptive capacity (World Risk Index 2012 ) Exposure Sensitivity Source: Garschagen and Romero-Lankao 2013 Climatic Change (in press) Adaptive capacity
    10. 10. Institutional Capacity for Climate Change Responses in Cities Patricia Romero-Lankao, Sara Hughes (USA) Angélica Rosas-Huerta (México), Roxana Borquez (Chile), Daniel Gnatz, (USA)
    11. 11. Why Santiago Chile and Mexico City? Climate and Environmental Change Santiago: Extreme temperatures (2045-2065 McPhee, et al. 2011 Mexico City: Precipitation Temperature increases Changes in precipitation Heat waves Droughts, flood s Magana, 2011
    12. 12. Why Santiago Chile & Mexico City? • Both share similar urbanization processes, reforms, and urban and environmental policies – E.g., due to population growth alone • Mexico City: 2007- 2030 available water per capita will diminish by 11.2% and in Santiago by 20.3 % per capita between 2005 - 2025 • Presence of scientific groups and multinational networks is key Why institutional response capacity? • Capacity for change has received increasing attention • Scholarship has mostly focused on – – • Yet, Frameworks distinguish between adaptive and mitigative capacity • Response capacity, an alternative, refers to – • Yet differences also exist – Mexico City is a frontrunner – Santiago is a laggard Motivations & barriers to adaptation Attributes of institutional capacity the broad pool of resources governmental and nongovernmental actors can use to reduce greenhouse gases and respond to climate variability and change (Burch and Robinson 2007)
    13. 13. Methods: Qualitative analysis 1. Interviews with Government (City, State, National), Academics, and NGOs/Community organizers a) b) 18 in Mexico City 22 in Santiago 2. Common coding scheme in Nvivo, network analysis software (UCINet) 3. Supplemented with government reports and academic studies
    14. 14. Unpacking institutional response capacity, a framework Source: Romero-Lankao, Hughes, Rosas-
    15. 15. Climate-relevant planning actions Outline Both cities at different stages of climate change planning time
    16. 16. Unpacking institutional response capacity, a framework Source: Romero-Lankao, Hughes, Rosas-
    17. 17. Administrative Structures and Networks • Mexico City • Santiago • Local (16 delegations), State (35 municipalities), and Federal authority • Term limits and political tension • Climate plan only for FD Environmental authorities • Local (52 communes), and Federal authority • Term limits and singleparty rule - Don’t interact as frequently with health & energy Don’t interact at all with housing, urban development, transportation)
    18. 18. Santiago Cities working networks; the size of nodes is proportional to the number of respondents reporting to work with that actor. Mexico City exhibits a relatively more integrated network. Mexico City Centralized yet fragmented administrative structure Government NGO Academic Private Supranati onal National State Local
    19. 19. Use of Information Mexico City Santiago • Virtual Climate Change Center • Early stages of generation • Top-down due to perceived lack of local capacity • Want information on climate scenarios • Top-down due to perceived lack of local capacity • Want information on local impacts and adaptation responses
    20. 20. Participation Mexico City Santiago • Authoritarian political culture (70 years PRI gov.) • Authoritarian political culture (Pinochet dictatorship, techno neoliberalism) • Mechanisms in place tend to be technocratic and paternalistic • Consultations, pamphlets and guidelines • Perceptions on this are mixed • Yet participation in civil protection and disaster management is more common
    21. 21. Opportunities • Leadership (and political ambition) • For Mexico City institutionalization of climate into planning • Presence of – Influential scientific groups – Non-governmental and international organizations – Participation of local authorities in transnational networks • Longer-term tradition of disaster management (although reactive)
    22. 22. Constraints • Centralized yet fragmented administrative structure inhibits effective coordination • Technocratic and top-down approach to information sharing inhibits learning and informed policy making at the city level • Limited existing mechanisms for participation in decision making transfer to climate change planning • Economic policies and efficiency dominate
    23. 23. Thank you!