Note: This is a free research I did,putting it here is big deal however if you wish to use it,let me know. The complete work is with me

Background

According to the anthropogenic GHG concentrations by country, Europe is responsible for 9% of the global carbon emission. Dividing up the impact of fossil fuel use (CO2 emissions) on climate change concerning the geographical coverage and the percentage emission from individual countries shows an estimate of the local impact based upon the present prevailing situations. The ecological footprint of any area is profoundly influenced by the food choice behavior of the inhabitants of that area. A person's food footprint is all of the emissions that result from the production, transportation, and storage of the food that is supplied to meet their consumption needs. In light of not only on projected CO2 concentration increases due to these emission rates but also on projections for future reductions through technological innovation; E/ENQS-2006-0218 shows how EU greenhouse gas policy might help ensure that decarburization can take place if necessary at a lower cost than it does now even though current technologies are too expensive or simply unsuitable today with few clear opportunities under different scenarios.

Research Question?

To Identify a predictive trend that appears to be associated with the carbon footprint in Europe. Various data have revealed different forms of relationships and models researchers have developed to understudy by how much per capital impacts CO2 changes differ across EU regions. This research project seeks to apply the GIS modeling technique and machine learning AI methodology to the study.

Methodology

Direct Remote Sensing (DR) Approach

A more spatially consistent way to model carbon footprint is to carry the satellite measurements directly to maps by calibrating them to field estimates of above ground biomass using any of a number of statistical or so-called "machine learning" techniques, such as neural networks or regression trees.

GIS tools for Spatial Analysis and Modeling

The predominant GIS tools for developing the model of source-sink matching are ArcGIS spatial analyst tools of which one of effective ones is the Cost-path tool. Cost-path calculates the lowest-cost path from a source to a destination: the output is the lowest-cost pathway between each source and its cheapest sink to reach.

Stratify & Multiply (SM) Approach

Another approach to map carbon footprint is to assign a single value (or a range of values) to each of a number of land cover, vegetation type, or other thematic map classes that have been obtained from satellite data and placed into categories.

Multi-sensor Collaboration

No single sensor on any satellite mission, regardless of whether radar, lidar or optical, can be anticipated to give reliably trustworthy evaluations of biomass, however utilization of these estimations in a synergistic manner might possibly beat the restrictions of every (whether radar immersion, lidar examining modes, or optical transient crisscrosses). Besides, some remote detecting perceptions might be more touchy to Above ground biomass(AGB) in explicit conditions (for example optical radiometry in more parched regions) or in regions with various AGB densities (for example lidar in thick damp woodland).

Plan of work and Time schedule

STEP 1: SELECTION OF THE TOPIC (ONE MONTH)

STEP 2: FORMULATION OF THE HYPOTHESES / QUESTIONS (TWO MONTHS).

STEP 3: PREPARATORY WORK.

STEP 4: COLLECTION OF THE DATA (SIX MONTHS).

STEP5: TREATMENT, PRESENTATION AND INTERPRETATION OF THE DATA (DATA ANALYSIS –

CONTENT ANALYSIS, PATTERNS, DRAW CONCLUSION – EXPLAIN WHY?) (ONE MONTH).

STEP 6: CONCLUSION AND EVALUATION (ONE MONTH).

STEP 7: REFERENCING OF SECONDARY SOURCES (FEW DAYS).

Bibliography:

1. United Nations Framework Convention of Climate Change: Issues relating to reducing emissions from deforestation in developing countries and recommendations on any further process. Submissions from Parties. 2006.

2. DeFries RS, Houghton RA, Hansen MC, Field CB, Skole D, Townshend J: Carbon emissions from tropical deforestation and re-growth based on satellite observations for the 1980s and 1990s. Proceeding of the National Academy of Sciences 2002, 99: 14256–14261. 10.1073 removed link

3. Research gate

4. Science direct

5. Scimago

6. Scopus