emissions. However in other contexts, differences between recommended values
and real achievement might be much higher. In such cases the level to be accounted
for the suppressed demand should be, whenever possible, the level achieved using
the technology or practices introduced, as long as it doesn’t exceed the agreed
minimum service level.
Linking differences in services levels to greenhouse gases emission may represent the major challenge to include the suppressed demand in climate change
mitigation evaluations. This evaluation used a simple linear regression model to
assess the suppressed demand. With an important work on the sampling, the sources
of variation have been minimized which allowed to achieve relatively good coefficient of determination considering the simplicity of the model. But this model
suffer from strong limitations and very low external validity. In the future, considering the important limits of the OLS to model the indoor temperature, the development of context specific dynamic thermal models appears as a relevant option to
account for the suppressed demand. However, housing conditions are heterogenious
in characteristics and therefore the model should be adaptable to a wide number of
houses using parameters that could be easily collected. Alternatively, default
factors of energy consumption could be developed to assess the baseline emission
levels for different levels of indoor temperature.
Applying the suppressed demand also requires important data collection and
significant equipment in the case of temperature monitoring. In the PSH case study,
the potential factors of variation between the houses are very important which
require a very carefull sampling. This study rely on an important household survey
as well as highly trained surveyors to assess the houses characteristics and select the
samples. In difficult contexts like Afghanistan undertaking long house intrusive
surveys comes with numerous challenges in terms of social acceptation and cost in
comparison of the budget for project implementation. To insure a high quality of
analysis, fuel consumption measurements had to be done 5 days a week during
8 weeks. In the Afghan context, the fact that a man cannot enter in a house when a
woman is alone and the security context of Kabul have strongly affected the study.
This led to numerous visits to the same household to gather data and in some cases
can lead to withdrawing some houses from the study.
Despite a limited immediate climate change mitigation potential, the investment
in energy efficient housing is crutial to achieve a low-carbon pathway and avoid a
critical carbon locking considering the time frame of housing investment. Future
research should focus on the development of suppressed demand models that could
be adaptable to different contexts. Emission default factors accounting for both
emission reductions and avoided emissions could help decision-making on climate
change mitigation policies by highlighting the significance of the emissions that
would result from long-term investments in carbon-intensive technologies. In
LDCs, successful climate change mitigation action requires to anticipate the
socio-economic development that will lead to investment in carbon-intensive
10 Integrating Avoided Emissions in Climate Change Evaluation Policies for. . .
185
and real achievement might be much higher. In such cases the level to be accounted
for the suppressed demand should be, whenever possible, the level achieved using
the technology or practices introduced, as long as it doesn’t exceed the agreed
minimum service level.
Linking differences in services levels to greenhouse gases emission may represent the major challenge to include the suppressed demand in climate change
mitigation evaluations. This evaluation used a simple linear regression model to
assess the suppressed demand. With an important work on the sampling, the sources
of variation have been minimized which allowed to achieve relatively good coefficient of determination considering the simplicity of the model. But this model
suffer from strong limitations and very low external validity. In the future, considering the important limits of the OLS to model the indoor temperature, the development of context specific dynamic thermal models appears as a relevant option to
account for the suppressed demand. However, housing conditions are heterogenious
in characteristics and therefore the model should be adaptable to a wide number of
houses using parameters that could be easily collected. Alternatively, default
factors of energy consumption could be developed to assess the baseline emission
levels for different levels of indoor temperature.
Applying the suppressed demand also requires important data collection and
significant equipment in the case of temperature monitoring. In the PSH case study,
the potential factors of variation between the houses are very important which
require a very carefull sampling. This study rely on an important household survey
as well as highly trained surveyors to assess the houses characteristics and select the
samples. In difficult contexts like Afghanistan undertaking long house intrusive
surveys comes with numerous challenges in terms of social acceptation and cost in
comparison of the budget for project implementation. To insure a high quality of
analysis, fuel consumption measurements had to be done 5 days a week during
8 weeks. In the Afghan context, the fact that a man cannot enter in a house when a
woman is alone and the security context of Kabul have strongly affected the study.
This led to numerous visits to the same household to gather data and in some cases
can lead to withdrawing some houses from the study.
Despite a limited immediate climate change mitigation potential, the investment
in energy efficient housing is crutial to achieve a low-carbon pathway and avoid a
critical carbon locking considering the time frame of housing investment. Future
research should focus on the development of suppressed demand models that could
be adaptable to different contexts. Emission default factors accounting for both
emission reductions and avoided emissions could help decision-making on climate
change mitigation policies by highlighting the significance of the emissions that
would result from long-term investments in carbon-intensive technologies. In
LDCs, successful climate change mitigation action requires to anticipate the
socio-economic development that will lead to investment in carbon-intensive
10 Integrating Avoided Emissions in Climate Change Evaluation Policies for. . .
185