Need for ICLAP
Sampling
World Urbanization Prospects dataset on urban agglomerations is used to shortlist 49 five-million-plus cities that represent the most populated and rapidly developing cities in the Asia-Pacific region. We purposefully include Melbourne (4,967,733) and Sydney (4,925,987), which are about to enter the five-million-population club. Thus, our sample of cities span across 16 countries, mostly in China (20) and India (9), followed by two each in Japan, Bangladesh, Pakistan and Australia. Many international frameworks—SDGs, Sendai Framework for DRR, Paris Agreement and the New Urban Agenda require human settlements information to feed indicators.
Based on the literature, we identify necessary indicators and sources for urban data, socio-economic data, energy and GHG data and urban climate plans which are extracted from city-specific agencies. The final dataset offers the location of each city, their extent (surface, shape), and describes each city with a set of geographical, socio-economic and environmental attributes for multiple data points spanning 25–40 years in time.
Methodology
The ICLAP model integrates established methods in climate mitigation, climate variability & adaptation and data science:
Spatial- Downscaling climate scenarios & GIS mapping of variability: Downscaling of climate variabilities at the sub-national/ urban level, would downscale temperature and rainfall anomalies from global/ regional MRI-CGCM3 and MICROC5 models (RCPs 4.5 & 8.5 scenario) to a scale of 50 km x 50 km for 2030, 2050 (Saraswat, et al. 2016).
Statistical- Trend analysis of urban indicators and GHG forecasts:
Bottom-up projections of city profiles (population, economic structures, energy consumption- buildings, waste, transport, GHGs) using CGE equilibrium modelling for 2030, 2050 (Fujimori et al. 2014).
Bibliometric- Meta-analysis of evidence from case studies:
Data science methods for systematic review of global case studies in local climate action, using WoS/ Google Scholar databases to conduct bibliometric analysis followed by meta-analysis of key policy solutions (Sethi et al. 2020, Lamb et al. 2018)