infrastructure gaps to manufacture and deliver the DLS products/services have been
filled. Many of these results are still preliminary, and not meant to convey an
empirical finding. Rather, they illustrate the kinds of insights such analysis enables.
For instance, the relative energy requirements for different DLS components differ
widely. Food in Brazil is far more energy intensive than in India, most likely due to
the preponderance of meat consumption in Brazil compared to India. The housing
energy requirement in India has a moderately higher share of capital turnover from
the expansion of the existing stock. However, the energy required to heat and cool
(largely cool) buildings is far greater in India due to, among other things, severe
climatic conditions (high temperatures and humidity). In both countries, energy for
overall capital expansion is dominated by housing and transportation.
Some caveats are in order. In general, in using existing country-specific data on
material and energy intensities to meet a single standard, we can’t discern whether
differences arise from different levels of service quality, differences in energy needs,
or differences in efficiency. For instance, health and education energy requirements
in Brazil are higher than in India, because energy intensity levels in Brazil are
significantly higher. This may imply that quality is higher, or that existing conditions (e.g., population density) may necessitate higher energy intensities in Brazil,
or that energy use is less efficient in Brazil. In subsequent work, some discernment
will be possible, based on deeper analysis of the health and education sectors.
Another caveat is that the electricity demand growth assumes power plants
already exist (that is, the embedded energy associated with building new electric
power plants to meet incremental electricity demand has not as yet been included).
This is, however, typically very small in comparison to operating energy.
5 Uncertainty Analysis
An important aspect of energy demand estimation is uncertainty and its attribution
to policymakers’ decision variables. We illustrate this for the case of housing and
water, which differ in the extent of influence of geography.
5.1 Housing Supply Energy Requirements
For housing, climatic conditions and building materials (accounting for local
availability) are key sources of uncertainty and are analysed in this section (see
Table 1 for all sources of uncertainty). We include country-specific options of
traditional mud-based construction in India, and wooden homes in Brazil. For this
paper, we use a single-storey building archetype for an average family in each
country. Due to different average household sizes, the sample house is 30 m
2 in
Brazil and 40 m
2 in India. Figure 3 shows the results, where the bars in the
402
N. D. Rao et al.
filled. Many of these results are still preliminary, and not meant to convey an
empirical finding. Rather, they illustrate the kinds of insights such analysis enables.
For instance, the relative energy requirements for different DLS components differ
widely. Food in Brazil is far more energy intensive than in India, most likely due to
the preponderance of meat consumption in Brazil compared to India. The housing
energy requirement in India has a moderately higher share of capital turnover from
the expansion of the existing stock. However, the energy required to heat and cool
(largely cool) buildings is far greater in India due to, among other things, severe
climatic conditions (high temperatures and humidity). In both countries, energy for
overall capital expansion is dominated by housing and transportation.
Some caveats are in order. In general, in using existing country-specific data on
material and energy intensities to meet a single standard, we can’t discern whether
differences arise from different levels of service quality, differences in energy needs,
or differences in efficiency. For instance, health and education energy requirements
in Brazil are higher than in India, because energy intensity levels in Brazil are
significantly higher. This may imply that quality is higher, or that existing conditions (e.g., population density) may necessitate higher energy intensities in Brazil,
or that energy use is less efficient in Brazil. In subsequent work, some discernment
will be possible, based on deeper analysis of the health and education sectors.
Another caveat is that the electricity demand growth assumes power plants
already exist (that is, the embedded energy associated with building new electric
power plants to meet incremental electricity demand has not as yet been included).
This is, however, typically very small in comparison to operating energy.
5 Uncertainty Analysis
An important aspect of energy demand estimation is uncertainty and its attribution
to policymakers’ decision variables. We illustrate this for the case of housing and
water, which differ in the extent of influence of geography.
5.1 Housing Supply Energy Requirements
For housing, climatic conditions and building materials (accounting for local
availability) are key sources of uncertainty and are analysed in this section (see
Table 1 for all sources of uncertainty). We include country-specific options of
traditional mud-based construction in India, and wooden homes in Brazil. For this
paper, we use a single-storey building archetype for an average family in each
country. Due to different average household sizes, the sample house is 30 m
2 in
Brazil and 40 m
2 in India. Figure 3 shows the results, where the bars in the
402
N. D. Rao et al.