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Analysis of CO2-emissions: Using multiple regression analysis to determine governmental systems and policies significance in reducing CO2-emissions
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2015 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

In the face of eagerness to act in favour of environmentalism, worldwide carbon dioxide emissions have increased. No signs of this trend breaking are available, until recently in 2014. Understandably, this poses questions about the role of government and environmental policies in reducing humankind’s carbon footprint. The aim of this thesis is to mathematically investigate the impact of governmental structures and climate related treaties on CO2-emissions. Out of the analysed factors, two were deemed statistically significant and their correlation with CO2-emissions were identified. These were percentage of renewable energy produced and corruption index. The first factor’s involvement being obvious as opposed to the latter.

Abstract [sv]

Trots en tydlig trend i viljan att agera miljömedvetet har koldioxidutsläppen i världen ökat. Ingenting har tidigare tytt på att utvecklingen skulle hämmas, utom nyligen 2014. Med bakgrund av detta är det begripligt att reflektera över den roll som en regering eller en internationell klimatöverenskommelse spelar i stävjande av koldioxidutsläpp. Detta kandidatarbetet tar sig an uppgiften att matematiskt undersöka den inverkan som faktorer i kategorin regeringstyp och klimatavtal har på utsläppen av koldioxid. Av alla de analyserade faktorerna kunde två urskiljas genom att vara statistiskt signifikanta och deras korrelation med koldioxidutsläpp identifierades. Dessa var andelen produktion av förnybar energi och korruptions index. Vad anbelangar klimatpåverkan är den första faktorns inblandning uppenbar till skillnad från den andra.

Place, publisher, year, edition, pages
2015.
National Category
Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-168214OAI: oai:DiVA.org:kth-168214DiVA: diva2:814845
Supervisors
Available from: 2015-05-28 Created: 2015-05-28 Last updated: 2015-05-28Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf