{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\n#for dirname, _, filenames in os.walk('/kaggle/input'):\n# for filename in filenames:\n# print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport geopandas as gpd\nimport seaborn as sn\nfrom compay_kpi import company_kpi\nfrom city_kpi import city_kpi","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Engagement for Monitoring and Planning of Water, Environment and Resilience (EMPoWER Framework)\n\n\n## Introduction\n\n\nThis analytical work is intended for submission against CDP’s competition on Kaggle for “Unlocking Climate Solutions: City-Business Collaboration for a Sustainable Future”. Here a framework is presented to address the problem statement of the competition. Intent of the work is to show how can a robust M&E framework be set-up at a city administration level so as to assess the overall situation in various relevant environmental and social dimensions for tackling climate change, and to measure or monitor programs/ initiatives that can be undertaken in collaboration with companies. \n\n\nFirst step in this work was analysing CDP survey data/ responses from all 3 questionnaires i.e. City, Company – Climate Change and Company – Water Security. However, it was felt that data was thin or open ended in most cases. For scientific interpretation and useful synthesis, need for leveraging external sources was deeply felt. For literature review, several research reports from reliable sources such as, UN agencies, World Bank, government bodies, research organizations, think tanks and academia were studies thoroughly before preparing the EMPoWER Framework. While this framework is pretty robust and scientific in terms of approach, it has scope for improvement in providing further data sources. Getting city level data for each important indicator is a challenge, therefore in some cases relevant sources for state level or national level data are provided. Publicly available data sources have been mentioned and used in this work, but it is understood that purchasing data or getting research data from organizations such as OECD, WRI, Notre Dame Global Adaptation Initiative, and Cities & Climate Change Initiative (UN Habitat), World Council on City Data, Rockefeller Foundation and ARUP would help in improving this piece of work. \n\n**Figure 1: City Climate Resilience Planning**\n\n\n*Source: “Guide to Public-Private Collaboration on City Climate Resilience Planning” by the Center for Climate and Energy Solutions, US (2017)*\n\n**Figure 2: Urban Sustainability Framework**\n\n\n*Source: Urban Sustainability Framework, Global Platform for Sustainable Cities, World Bank (2018)*\n\n\nAlthough there is a plethora of existing relevant research, due to paucity of time and space, only limited resources are being quoted. Figure 1 shows how to go about climate resilient planning at city level through business collaboration. Whereas Figure 2 depicts a comprehensive approach for dimensions and measurement framework. On the other hand, Figure 3 presents an interesting visualization where cities are presented on the world map along with observed temperature rise, population size and growth rate. While these literatures have been of enormous help for enriching knowledge on the given subject, a custom made framework has been crafted specifically for the purpose of this challenge. \n\n**Figure 3: Large urban agglomerations with observed climate change during 1901–2012**\n\n\n*Source: Fifth Assessment Report of IPCC (2014)*\n\n\n\n## Description\n\n\nThe EMPoWER Framework has 17 sections in total. Each of these sections is for one area or theme, e.g. Energy Consumption & Mix, and Resilience. Section # 1 to 16 of the framework are for understanding the “big picture” at city level. Section # 17 is quite different from it. It focuses on the “micro lens”. It is for highlighting potential areas of collaboration between cities and companies. It is important to note that these KPIs (in Section #17) are to be measured at company level. However, these can be aggregated at city level for all contributing companies. Common units of measurement and formula are important for aggregation logic. To read the framework, one has to look at the area/ theme/ component followed by relevant indicators/ metrics. \n\nLogic model is a useful tool for development intervention planning and monitoring; and it is widely accepted in international development sector or social impact space. EMPoWER Framework has alignment with logic model/ LFA in the sense that from Section # 1 to 16, areas and indicators/ metrics mentioned against them are largely at Outcome or Impact level. But points mentioned in Section # 17 under “Areas of Collaboration” are at Activity or Output level. \n\nIt is important to mention here that last two points i.e. Serial#17(p) “ESG Performance” and Serial#17(q) “Impact Investing” are relevant in terms of understanding interest level of a potential partner company in addressing social or environmental issues in the broader realms of climate change. \n\nThis piece of work has 3 parts. In the first part, EMPoWER Framework is given in tabular format. Second part has data visualization for select points of the framework. Third part contains data visualization in two grids. One has synthesis of city survey data and another has gist of company survey data. It is envisaged that these two grids would provide intelligence for partner matching and also to unlock potential areas of collaboration. When areas of collaborations are mutually agreed upon between the city administration and collaborating company, then the indicators/ metrics mentioned for the area/ theme could be used to track the intervention program. \n\nData sources wherever publicly available are mentioned in the later part of this submission. City administrations/ municipalities are encouraged to build capacity and set-up suitable system to track these KPIs for institutionalizing the proposed framework with suitable customization. \n\n\n## Framework\n\n\nEMPoWER Framework is presented in a nutshell in the following table:\n\n\n\n\n\n\n\n## Data Visualisation\n\n\nAlthough the US was kept in mind while preparing the framework and creating data visualizations, it can be said with confidence that with adequate data this model can be applied on any country. Some data visualizations are shown here as examples. This is just to provide some illustrations to present data in a useful way for generating meaningful insights.\n\n**Figure 4: Climate Risk Index (US Cities)**\n\n\n*Source: Visualisation created by authors using Python*\n\n\nThe above exhibit shows a city’s risks from climate change. On the visualization, darker the colour higher the risk is. This is adapted from a research on urban adaptation assessment carried out by Notre Dame Global Adaptation Initiative. Here, Risk is considered as a factor of Exposure, Sensitivity and Adaptive Capacity. Exposure has strong correlation with the number of individuals and critical infrastructure exposed to climatic or environmental hazards. For extreme cold, heat, flooding and sea-level rise, Sensitivity refers to the degree to which population of the city are affected by climate hazards. Adaptive Capacity refers to the city’s ability to respond to the consequences of climate hazards. In the US, Santa Ana (California), Hialeah (Florida) and Paterson (New Jersey) have highest level of climate risks. Data sheet is given in Annexure/ as enclosure.\n\nChart below has Population Density of the US cities on X-axis and the corresponding Climate Risk Index on Y-axis. It shows strong correlation between these two variables. Therefore, it is understood that cities with higher population density often has higher risks from climate change, disasters or environmental hazards. \n\n**Figure 5: Climate Risks vs Population Density**\n\n\n*Source: Visualisation created by authors using Python*\n\n\nModern human civilization is undergoing an unprecedented situation in the form of Covid-19. This pandemic has created the biggest upheaval after the Second World War. Map below shows the US cities graded as per the Covid-19 Vulnerability Index. It clearly points out that this public health emergency has affected the habitats of African-American community much more than other areas. For instance, Gary (Indiana) which has nearly 84% people from this community is the most vulnerable as per the index. Data sheet is given in Annexure/ as enclosure.\n\n**Figure 6: Covid-19 Vulnerability Index (US Cities)**\n\n\n*Source: Visualisation created by authors using Python*\n\n\nAccording to CDC, “*Every community must prepare for and respond to hazardous events, whether a natural disaster like a tornado or a disease outbreak, or an anthropogenic event such as a harmful chemical spill. The degree to which a community exhibits certain social conditions, including high poverty, low percentage of vehicle access, or crowded households, may affect that community’s ability to prevent human suffering and financial loss in the event of disaster. These factors describe a community’s social vulnerability.*” For climate vulnerability assessment, susceptibility to poor people is very important. Thus CDC’s Social Vulnerability Index 2018 is pretty helpful in this regard. \n\n**Figure 7: Social Vulnerability Index (US Counties)**\n\n\n*Source: Visualisation created by authors using Python*\n\n\nTable below shows top 32 US counties that got more than 99 percentile score on the Social Vulnerability Index. It is important to note that all these counties have significant population below the poverty line. Per capita income is abysmally low in these counties, in comparison to the national figure which is USD 65k according to the World Bank’s latest estimate. \n\n\n\nDatasets from the research of Notre Dame Global Adaptation Initiative have been used to create the following exhibit that helps to assess resilience of the US cities. Here, Readiness is a function of economic, governance, and social readiness. It shows how ready a city is in terms of climate change adaptation. In the US, Ann Arbor (Michigan), Madison (Wisconsin) and Berkeley (California) are top 3 cities as per the climate readiness index.Data sheet is given in Annexure/ as enclosure.\n\n**Figure 8: Climate Readiness Index (US Cities)**\n\n\n*Source: Visualisation created by authors using Python*\n\n\nIn this regard, it is important to mention that architecture and civil engineering consulting firm ARUP and Rockefeller Foundation have prepared something called City Resilience Index. However, the data is not widely available and the approach may not be fully aligned with the purpose of this CDP - Kaggle challenge. Thus as far as Resilience in EMPoWER Framework is concerned, adapted version of ND-UAA as depicted in the map would be relevant.\n\n\n## Conclusion\n\n\nAnalysis of CDP survey dataset helped in generating interesting insights. Grid of City Score has component wise for each city. This has been done based on company headquarter location from CDP data. Colour grading on cells has been done basis the score. Ligher colour represents better score. It basically means that those components are already on high focus. While low score certainly indicates need for collaboration, higher score does not rule out the scope for collaboration as some cities want to go further on certain area. Grid of Company Score also has component wise score and colour grading, along with headquarters. Ideally, locations of operations, offices and sites should also be part of this analysis. But due to lack of standard data and paucity of time, these could not be taken into consideration. \n\nWe have selected few questions from the city survey questionnaire based on their nature and focus area provided by CDP, and assigned weight against them based on their relative importance to develop the below grid. One can enter the city name in below function to get the city metrics(e.g. city_kpi(city_name=['New York City, 'Salt Lake City']), however it will show first 5 cities in the data if we don't mention city name. Please refer the *city_kpi.py* utility files for detailed calculation."},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"city_kpi(city_name=['New York City', 'City of Philadelphia'])\n#city_kpi() -- to see top 5 cities","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"We have also developed similar grid for companies following by selecting questions from company questionnaires. Company grid can be used as an input to decide the potential partners. Please refer the company_kpi.py utility files for detailed calculation."},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"company_kpi()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Following mapping points to broad strategy as far as the area of collaboration is concerned:\n\n\n\nThe above mapping emphasizes that improving performance on any component can be done by choosing suitable area of focus for collaboration with companies. KPIs for these possible areas of collaboration have been detailed out in the framework section. It is expected that the EMPoWER Framework would be taken seriously for adoption amongst relevant stakeholders. It is open to refinement, further augmentation and wider propagation. Authors/ creators of the framework would be more than happy for any consultation or engagement in this regard. \n\n## Data Sources\n\nWe have uploaded the exernal data used for the visualisations, and scores for all cities and companies [here](https://www.kaggle.com/bhaskardutta29/external-data-dp-comp)\n\n\n\n\n*Wherever possible and publicly available, data sources are mentioned against identified KPIs. It can be further enriched with data sources for remaining KPIs by data collation and compilation through collaboration with relevant organizations or through new research.*\n\n---"}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}