- Environmental Sciences
- Greenhouse gas emissions analysis: Collection, analysis, and interpretation of data on CO2 emissions categorized by sector and country.
- Environmental Impact Assessment: Evaluation of CO2 emissions from specific sectors and countries.
The urgency of environmental concerns linked to global warming has escalated, presenting significant threats to the environment upon which human beings rely for their survival. Carbon emissions are a crucial aspect of both environmental and economic concerns. While it is essential to identify the factors that drive carbon emissions, it is also relevant to understand how the different roles of countries at a global level influence these emissions. Countries play diverse roles in the production of carbon emissions, and these roles may vary over time. It would be of interest to understand further their roles by examining their features in a network. Conclusions might point to ways for reducing emissions. Understanding the impact of country positions within global supply chains on carbon emissions would indeed be helpful in formulating effective strategies for reducing greenhouse emissions. By analyzing a network of CO2 emissions involving countries and sectors, we can discern the key factors contributing to emissions and explore the potential for improving emission reduction actions. The objective of this project is to explore and interpret the characteristics of two networks in order to understand the relationships between countries and industrial sectors and identify any relevant patterns or groups of interest. Various Network Analysis measures will be applied to examine the characteristics of the network and identify relevant nodes and connections. The available data span from 1970 to 2022, allowing for comparisons between different analyses to obtain a more comprehensive picture. An analysis of two networks corresponding to different time periods will be therefore conducted to examine the evolution of country roles.
The project will use the IEA-EDGAR CO2 dataset, a component of the EDGAR (Emissions Database for Global Atmospheric Research) Community GHG database version 7.0 (2022). The dataset includes or is based on data from IEA Greenhouse Gas Emissions from Energy, available at www.iea.org/data-and-statistics , as modified by the Joint Research Centre. EDGARv7.0 ( https://edgar.jrc.ec.europa.eu/dataset_ghg70#intro ) is the first product of the new EDGAR Community GHG emissions database, which is the outcome of an agreement between JRC and IEA to work more closely to gather to provide consistent harmonised CO2 emissions from fossil fuel combustion. It provides estimates for emissions of the three main greenhouse gases (CO2, CH4, N2O) and fluorinated gases per sector and country (https://edgar.jrc.ec.europa.eu/dataset_ghg70#p1 ). For the purpose of this project, the considered data are the one regarding the CO2 emissions.
Two bipartite networks will be created, representing countries and industrial sectors at different points in time, with edges connecting each country to the industrial sectors responsible for their emissions. Edge weights will be based on the quantities of CO2 emissions per sector. The results of the measures applications on the two networks will be compared to search for relevant differences in the selected time periods in order to highlight the changing of the emissions network features.
The following Network Analysis measures will provide a comprehensive perspective on the roles and importance of countries and sectors in the CO2 emissions network and their interactions.
When calculating node centrality measures in bipartite networks, a possible approach is to apply PageRank on the one-mode projection of the network. However, the projection could cause information loss and distort the network topology. Therefore, for better node ranking on bipartite networks, it has been chosen a ranking algorithm that fully accounts for the topology of both modes of the network. The BiRank package ( https://pypi.org/project/birankpy/ ), which implements bipartite ranking algorithms, will be used to perform the Pagerank on the bipartite Networks. (Reference: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8559594/ ) For performing the analysis it will also be taken into account the weighted nature of the networks. Therefore another centrality measure for weighted networks, the Laplacian centrality, will be computed through Networkx (reference: https://www.sciencedirect.com/science/article/abs/pii/S0020025511006761 ).
Simrank measure performedwith Networkx will allow to provide insights into the degree of similarity between countries in their emissions behavior within shared industrial sectors, in order to identify similar countries and distinguish between the most impactful ones and the least impactful ones.