We're sorry but this page doesn't work properly without JavaScript enabled. Please enable it to continue.
Feedback

Transitions in pathways of human development and carbon emissions

00:00

Formale Metadaten

Titel
Transitions in pathways of human development and carbon emissions
Serientitel
Anzahl der Teile
16
Autor
Lizenz
CC-Namensnennung 3.0 Unported:
Sie dürfen das Werk bzw. den Inhalt zu jedem legalen Zweck nutzen, verändern und in unveränderter oder veränderter Form vervielfältigen, verbreiten und öffentlich zugänglich machen, sofern Sie den Namen des Autors/Rechteinhabers in der von ihm festgelegten Weise nennen.
Identifikatoren
Herausgeber
Erscheinungsjahr
Sprache

Inhaltliche Metadaten

Fachgebiet
Genre
Abstract
Countries are known to follow diverse pathways of life expectancy and carbon emissions, but little is known about factors driving these dynamics. In this letter we estimate the cross-sectional economic, demographic and geographic drivers of consumption-based carbon emissions. Using clustering techniques, countries are grouped according to their drivers, and analysed with respect to a criteria of one tonne of carbon emissions per capita and a life expectancy over 70 years (Goldemberg's Corner). Five clusters of countries are identified with distinct drivers and highly differentiated outcomes of life expectancy and carbon emissions. Representatives from four clusters intersect within Goldemberg's Corner, suggesting diverse combinations of drivers may still lead to sustainable outcomes, presenting many countries with an opportunity to follow a pathway towards low-carbon human development. By contrast, within Goldemberg's Corner, there are no countries from the core, wealthy consuming nations. These results reaffirm the need to address economic inequalities within international agreements for climate mitigation, but acknowledge plausible and accessible examples of low-carbon human development for countries that share similar underlying drivers of carbon emissions. In addition, we note differences in drivers between models of territorial and consumption-based carbon emissions, and discuss interesting exceptions to the drivers-based cluster analysis.
Durchführung <Elektrotechnik>NiederspannungsnetzVideotechnikEmissionsnebelWarmumformenTheodolitWeltraumErdefunkstelleBlatt <Papier>ComputeranimationBesprechung/Interview
BraunALICE <Teilchendetektor>WarmumformenEmissionsvermögenDurchführung <Elektrotechnik>EnergieniveauTiefdruckgebietBrennpunkt <Optik>ComputeranimationBesprechung/Interview
Durchführung <Elektrotechnik>EmissionsnebelKlimaänderungDurchführung <Elektrotechnik>BrechzahlErwärmung <Meteorologie>TreibhausgasSeeschiffPatrone <Munition>Besprechung/Interview
Durchführung <Elektrotechnik>EmissionsvermögenBlatt <Papier>RaumfahrtWarmumformenLeistenKalenderjahrPassatBesprechung/Interview
PassatFörderleistungPassatEmissionsvermögenRaumfahrtComputeranimation
AntennendiversityIonFACTS-AnlageBesprechung/Interview
MikroklimaBesetzungsdichteEmissionsvermögenGruppenlaufzeitKlimaänderungBasis <Elektrotechnik>TreibhausgasSchraubendreherBöttcherFaraday-EffektClusterphysikBesprechung/InterviewComputeranimation
GruppenlaufzeitClusterphysikComputeranimation
EnergieniveauTiefdruckgebietEmissionsvermögenBesprechung/Interview
GroßkampfschiffEmissionsnebelEisenkernSattelkraftfahrzeugKlimaGruppenlaufzeitTiefdruckgebietTreibhausgasBuntheitKalenderjahrClusterphysikSchraubendreherKlangeffektDreidimensionale IntegrationFACTS-Anlage
Blatt <Papier>ErsatzteilBesprechung/Interview
KlimaDurchführung <Elektrotechnik>ProfilwalzenEmissionsvermögenErdefunkstelleTrajektorie <Meteorologie>SchraubendreherKlimaDurchführung <Elektrotechnik>GruppenlaufzeitTiefdruckgebietSource <Elektronik>FernordnungLuftstromComputeranimation
Transkript: Englisch(automatisch erzeugt)
Hello, I'm William Lapp at the Tyndall Centre for Climate Change Research, University of Manchester. The research I'd like to talk about today is a paper co-authored by myself and colleagues called Transitions and Pathways of Human Development and Carbon Emissions. The work started on the assumption that there's a very strong relationship between emissions
and development achievement at the national level. So our goal is to understand how this relationship works, what are the differences between countries, and in particular focus on countries that are achieving very good outcomes in terms of their development achievement, but at the same time low emissions,
and what this means for global climate change policy. To do this, we looked at global datasets for carbon emissions and development. But how did we actually measure development? So rather than looking at economic indicators such as GDP and income, which have received much criticism lately, we chose instead to use national life expectancies, which is arguably a less abstract and more direct way to understand real development outcomes.
Another unique feature of this paper is the emissions dataset we used. So standard territorial emissions, those that count only the CO2 produced within each country, are becoming increasingly compromised due to the international trade in goods and services.
Instead, we chose to use recently produced datasets that adjust national emissions for this flow of carbon embodied in trade. While we're not just interested in comparing countries based on their outcomes of emissions, we'd like to take account of the diversity in factors such as demographic, economic, and geographic factors
that might influence those emissions levels. First by exploring the literature, then using regression analysis on the consumption based emissions data, we selected a group of variables that would form the basis of a cluster analysis. We found significant effects for income, the ratio of exports to GDP, population growth, and climate,
but no significant contribution for urbanization or population density. We concentrated on the significant drivers of carbon emissions we found to group the countries into clusters, each group sharing similar challenges or opportunities for decarbonization. Once we had identified these five clusters of countries, we went back to our original question.
How do they perform in terms of enabling higher life expectancy at low levels of carbon emissions? On this graph, you can see all the countries in our analysis, plotted in terms of carbon emissions per capita on the horizontal axis, and life expectancy in years on the vertical axis.
Each cluster has its own general identity in terms of the carbon emissions drivers. The region on this graph of most interest to us, particularly for climate policy, is the upper left hand corner, above 70 years of life expectancy, but under one ton of carbon emissions per capita. This corner indicates environmental sustainability in terms of low carbon emissions,
and social prosperity in terms of high life expectancy. We call this region Goldenberg's Corner. We might expect all the countries here to be of a single type, and we see many colors indicating many clusters of carbon emission drivers are represented. In fact, all groups of countries, except for the very high income category, are represented here. We have three key messages from this paper.
First, our criteria for social rather than economic performance offers some hope to nations seeking to reduce their emissions, particularly for those that share the similar underlying drivers of those emissions, and hence, barriers to decarbonization as those within Goldenberg's Corner. Second, the absence of wealthy nations from this group of high performers
gives some cause for concern. As we can see, high human development is compatible with climate goals, while high national income is not. Third, comparing countries and their pathways of development offers a rich source of information about plausible low carbon trajectories. Future research may be informed by rates of change in emissions and development outcomes,
in order to broaden our understanding into the dynamics of these pathways of development.