Team 13 Mission 1

Potential questions that Team 13 would like to explore:

• Could it be that the kinks in the data indicate a turning point in  the countries economy/political history? For example, what happened in  China in the year 2000? The rate of C02 emissions over time has almost  doubled compared to pre-2000. +1
  • Gus: This seems like a natural question ... perhaps look at other similar patterns in other countries?
  • Is this asking are emissions and economic growth related?
  • More "how" are they related (lag, slope, etc) and maybe it's not growth per se but energy generation capacity.
• Why have some countries managed to reduce their CO2 emissions? And why are others steadily increasing or at least peaking at certain time periods?
• What is the projections for specific countries and with what confidence can we stand by those projections?
• What reductions goals are in place in certain countries….do they have climate policy’s that have been influential in CO2 reduction or potential reduction? What are the G8 nations doing to reduce carbon emissions? +1
• What fuel type presents the most risk of increasing CO2 emissions – is it from industry, transport, residential, commercial etc. So analysis by sector /sources such as Industry, Transport, Agriculture might provide us with some indication whether it is from a specifc sector that an increase or decrease is found.
• Related to this maybe also is analysis by each individual fuel (petroleum, natural gas, coal). Can we get data to help us decipher or unpick the data story?
• Analysis by country (and country groupings), nations, global patterns and comparisons to world data.  Should we select one of the interesting data patterns and do a case study of that country….how has that country reduced its emissions…maybe probe deeper why? +1
  • Gus: I like this one as it would allow us more especulative work but we'd need more data so we'd have to decide on an area/country beforehand.
• A visualisation of a country that has emission problems and maybe a story of how they have improved? [ref to http://www.mediacoop.ca/sites/mediacoop.ca/files2/mc/imagecache/thumb200/co2_global_emissions.jpg]
• What is the relationship between the "global economy" and the emissions? (how to quantify the "global economy"?) 
• Are emissions related per capita (VR) and GDP per capita??(Gus)
• 192 (out of 224) countries have adoped the Kyoto protocol (to have targets for Co2 emissions). Although the remaining countries are small what is the longitudinal pattern for them? So possible analysis by countires inside and outside the Kyoto protocol....or at least worthy of a look?
• The Guardian Article reveals that the biggest decrease in emissions is Ukraine (at 28%) and the Cook Islands has the highest increase (approximately up to 67%) - why ?? Should we use these examples as interesting case studies?
• Depending on how you look at the data, different stories can emerge - can we make comparisons with other useful data sources and almost try to triangulate (might be complex to do)....maybe like an onion there are different layers we can explore?
• Just found this article (http://www2.lse.ac.uk/geographyAndEnvironment/research/Researchpapers/rp83.pdf) that examines the role of geographical factors as determinants of CO2 emissions. Maybe we should be asking if geography is linked to emissions, e.g. countries that face colder winters might have higher heating needs than those with milder winters. Possibly an interesting factor to explore?

Notes: Maybe as we add to the list we should begin to choose the top 3 key questions that we would like to find out from the data....or that we think are key impact factors? We could agree that between us first and then see if we need any other related information. (Vilinda)
Gus had noted this key impact factor which may be useful as a starting point??

Data needed: Country GDP per year

Location: http://data.worldbank.org/indicator/NY.GDP.PCAP.CD

Reason: Explore relationship between CO2 emissions and GDP

Team 13 (Team members - Vilinda, Gus and Shrividya)

Mission 1: Questions
1. Are CO2 emissions related to the wealth of the nation or GDP per capita?
Team action(s): Explore relationship between CO2 emissions and GDP.
Also, explore the relationship by population of the country (per capita)?

2. Is decline in CO2 emissions related to (a) economic growth (b) energy use (c) fuel type and/or (d) government policy or other initiatives put in place by country, nation?
Team action(s): Explore the decline in CO2 emissions in relation to possible key impact factors.

3. Is geographical factors related to CO2 emissions? Do countries that endure colder winters or warmer climates have more or less CO2 emissions?
Team action(s): Explore the relationship between geography and CO2 emissions and possibly climate change factors?

Some of these questions could be explored by case study provided by a specific country??

General Question: It is likely that a combination of various factors produce changes in the rise or fall of CO2 emissions. If so, how do we unpick these factors to tell a coherent story?



Mission 2: Cleaning the Data 

Some comments (Shrividya)

Summary
- I have been trying to analyse and visualise the main spreadsheet in R. The misison is centered around spreadsheet analysis but I believe that R is a more flexible solution. It includes a lot of functions for data munging as well as some neat visualisation libraries. 

What I found
- Loading the data into R was a little non-trivial. I had to load the data and the column names separately due to poor parsing of the column names by the read.csv function. 
- To visualise the top N (N > 1) CO2 emitters in 2009, I had to remove 'NA' from this column alone before plotting the data. See the following plot (in shared dropbox folder) for a time series of the top 5 emitters of 2009

To do:
- Legend for plots! (still learning R) ;-)
- plot the contribution from different fuel sources for the top N emitters. I have downloaded the data but I still need to look at it. 
- identify important features in time series data. Research connections between features and indicators like population, GDP etc. 

Some Comments (Vilinda)
What I have done: 
Data: I have looked at the main data set (Total CO2 Emissions) and the Per Capita CO2 data. I have been trying to get acquainted with the data at a basic level; have looked overall whether the 'change in place' (last column) shows any pattern for whether emissions have declined, increased or stayed the same. From the list provided, Down is 80; Up is 81 and Same is 59 so a very similar pattern of decline as well as increase. I sorted the data in a separate worksheet to do that. 
I also looked at removing the missing data and N/A in the Per Capita worksheet. Again I removed them in a separate worksheet/workbook.
Missing Values: Have looked how you would deal with missing values in Excel. 
Duplicates: I also looked for duplicates. Couldn't find anything apart from Germany and Germany East and West. I had never heard of 'Reunion' (it is an island).

Challenges: So far the challenges I have are 
(a) There is a lot of information to take in the main data set
(b) My geography has never been that good when I am looking at the data without a visual :)
(c) Accuracy of doing spreadsheet actions - my lack of confidence is making me take small sections of data and looking at them closely. Probably a good thing when it is not your own data!!
(d) Related to (c) above this is more time consuming than I thought 
(e) I usually use Excel only for graphics and very seldom so my knowledge/skills are very limiting with this package. 

Will do more when I am back in the office which is Wednesday