ESSA

ESSA

Can Changing the Population Change Climate?


Alice He

By

September 12th, 2012


People can change the climate by not changing anything at all.


The intergovernmental panel on climate change (IPCC) in its special report on emission scenarios (SRES) attempted to predict increases in global surface temperature under different social, economic and demographic conditions. Broadly speaking, they identified a group of scenarios to capture and generalise future climate change in different conditions.

The scenarios can be grouped together in 4 umbrella categories They are as follows:

A1
The A1 storyline and scenario family encapsulates rapid economic growth, global population peaking in mid-century and declining after, and rapid introduction of new and more efficient technologies. Its major characteristics include convergence among regions, capacity building and more cultural and social interaction, and less disparity in regional differences of per capita income.

A2
Often nicknamed ‘business as usual’, the major themes of this scenario family is the self-reliance and preservation of local identities, a continuously increasing population (with fertility patterns across regions converging slowly) and economic development predominantly regionally oriented. Economic growth and technological change is more fragmented and slower than other story lines. It posits a heterogeneous world with unequal progress across countries.

B1
The B1 storyline is much like A1 except for its defining feature: a rapid shift towards a service and information based economy. Material production is reduced while clean and resource-efficient technologies are introduced. However there are no additional climate initiatives; scenarios do not assume implementation of the United Nations Framework Convention on Climate Change or the emissions targets of the Kyoto Protocol.

B2

B2 scenarios have an emphasis on local solutions to economic, social and environmental sustainability, global population is continuously increasing (but at a rate slower than A2) with intermediate levels of economic development and less rapid, more diverse technological change than in B1 and A1. It is oriented towards environmental protection and social equity but on a local and regional level.

In the above graph, solid lines depict the average of surface temperature rises predicted by many climate models, different coloured lines represent the different averaged predictions under different scenarios, and shading represents the +/-1 standard deviation of model predictions. The grey bars on the right indicate the most likely range and the best estimate (solid line within each bar) of global surface warming for each scenario. The six scenarios graphed are:

  • B1
  • A1T: A1 but with an emphasis on non-fossil energy sources
  • B2
  • A1B: A1 but with balance across energy sources used
  • A2
  • A1FI: A1 but with an emphasis on fossil-intensive energy sources

While the SRES scenarios take into account changes in population growth, additional research highlights potentially significant changes in global climate by considering more delicate demographic trends and its effects on carbon emissions. Niell et al. (2010) used a Population-Environment-Technology model (PET) to incorporate demographic dynamics in climate modelling. To capture the effects of demographic change on climate more accurately, they approached it by analysing characteristics of households through:

  • Household age (defined as the age of the household head)
  • Size (number of members)
  • Urban/rural residence in each region (from a total of 9 regions used to model the global economy)

Data from household surveys was used to compute global household projections and the corresponding effect on carbon emissions. Within the PET model, households can affect emissions either directly through their consumption patterns or indirectly via their effects on economic growth; these are captured by the effects of,

  • Population growth rates on economic growth rates
  • Age structure changes on labour supply
  • Urbanisation on labour productivity
  • And anticipated demographic change (and its economic implications) on savings and consumer behaviour

Graphs A and B show the contributions towards carbon emissions by changing demographics – separated into the effects of changing household age (age of the household head), household size, urbanisation and the combined influence of all three. B shows the amount of carbon emissions relative to the baseline scenario which uses the UN’s projected medium population growth trend. Demographic changes under the A2 and B2 SRES storylines are considered.

The results indicate that aging can reduce emissions in the long term by up to 20%. The PET model suggests that an aging population reduces labour productivity or labour force participation, leading to slower economic growth. Urbanisation on the other hand can increase emissions by up to 25%; the higher productivity of urban labour implies that urbanisation brings higher levels of economic growth. In addition, slowing population growth could provide 16-29% of the emissions reductions often cited as necessary by 2050 to evade dangerous climate change, by the end of the century the reduction could be as large as 37-41%.

Therefore, demographic policies can have a hand in altering climate change too – this is by recognising the effects of an aging population in labour productivity and changing consumption preferences due to urbanisation. Because of the household-centric approach used in this demographic analysis, solutions to climate change can also be extended to include policies on family planning – for example, changes to reproductive health services will affect fertility trends.

 

Further reading:

IPCC summary for policymakers: http://www.google.com.au/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CDcQFjAA&url=http%3A%2F%2Fwww.ipcc.ch%2Fpdf%2Fassessment-report%2Far4%2Fwg1%2Far4-wg1-spm.pdf&ei=oUlMUJLdCK6fiAfroICIDA&usg=AFQjCNH3kgAs4cAqOcmHrYLSRHIUIbkouw

Global demographic trends and future carbon emissions (Niell et al., 2010): http://www.pnas.org/content/107/41/17521.full.pdf+html

The views expressed within this article are those of the author and do not represent the views of the ESSA Committee or the Society's sponsors. Use of any content from this article should clearly attribute the work to the author and not to ESSA or its sponsors.

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