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There are very few useful work and exergy analysis studies for China, and fewer still that consider how the results inform drivers of past and future energy consumption. This is surprising: China is the world's largest energy consumer, whilst exergy analysis provides a robust thermodynamic framework for analysing the technical efficiency of energy use. In response, we develop three novel sub-analyses. First we perform a long-term whole economy time-series exergy analysis for China (1971–2010). We find a 10-fold growth in China's useful work since 1971, which is supplied by a 4-fold increase in primary energy coupled to a 2.5-fold gain in aggregate exergy conversion efficiency to useful work: from 5% to 12.5%. Second, using index decomposition we expose the key driver of efficiency growth as not 'technological leapfrog-ging' but structural change: i.e. increasing reliance on thermodynamically efficient (but very energy intensive) heavy industrial activities. Third, we extend our useful work analysis to estimate China's future primary energy demand, and find values for 2030 that are significantly above mainstream projections.
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Understanding China’s past and future energy demand: An exergy
efficiency and decomposition analysis
Paul E. Brockway
, Julia K. Steinberger, John R. Barrett, Timothy J. Foxon
Sustainability Research Institute, School of Earth and Environment, University of Leeds, LS2 9JT, UK
We complete the first time series exergy and useful work study of China (1971–2010).
Novel exergy approach to understand China’s past and future energy consumption.
China’s exergy efficiency rose from 5% to 13%, and is now above US (11%).
Decomposition finds this is due to structural change not technical leapfrogging.
Results suggests current models may underestimate China’s future energy demand.
article info
Article history:
Received 12 February 2015
Received in revised form 3 May 2015
Accepted 22 May 2015
Energy efficiency
Energy demand
Useful work
There are very few useful work and exergy analysis studies for China, and fewer still that consider how
the results inform drivers of past and future energy consumption. This is surprising: China is the world’s
largest energy consumer, whilst exergy analysis provides a robust thermodynamic framework for analys-
ing the technical efficiency of energy use. In response, we develop three novel sub-analyses. First we per-
form a long-term whole economy time-series exergy analysis for China (1971–2010). We find a 10-fold
growth in China’s useful work since 1971, which is supplied by a 4-fold increase in primary energy cou-
pled to a 2.5-fold gain in aggregate exergy conversion efficiency to useful work: from 5% to 12.5%. Second,
using index decomposition we expose the key driver of efficiency growth as not ‘technological leapfrog-
ging’ but structural change: i.e. increasing reliance on thermodynamically efficient (but very energy
intensive) heavy industrial activities. Third, we extend our useful work analysis to estimate China’s future
primary energy demand, and find values for 2030 that are significantly above mainstream projections.
Ó2015 The Authors. Published by Elsevier Ltd. This is an open accessarticle under the CC BY license (http://
1. Introduction
As the world’s economic powerhouse and largest energy
consumer [1], much effort is spent understanding China’s historical
energy consumption (e.g. [2–4]) and future energy demand [5–7].
However these studies typically examine primary or final energy
data, rather than useful work values obtained using an exergy anal-
ysis based technique. This is the research gap that this paper seeks
to address. Exergy analysis takes a broader, whole system
approach to energy analysis, giving ‘‘a measure of the thermody-
namic quality of an energy carrier’’ (p. 686, [8]), thereby enabling
a robust view of useful work consumed in provision of energy ser-
vices. Exergy analysis also has the benefit of taking into account
more aspects of the energy supply chain than traditional energy
analysis, and in a more consistent way. A flow visualisation of pri-
mary exergy to useful work is given in Fig. 1.
A key assumption in this study is that useful work is a better
‘energy parameter’ than primary energy on which to analyse end
energy use and economic activity, since – as Fig. 1 shows – it is
the last thermodynamic place where energy is measured before
it is exchanged for energy services. We are not alone in this view.
Numerous authors (e.g. [8–10]) suggest second law exergy analy-
ses can help understand national-scale energy use. For economic
insights, Percebois [11] suggested in 1979 that energy intensity
metrics (i.e. energy consumption relative to GDP) were better
undertaken at the energy output stage, since it ‘‘allows us to anal-
yse structural change in energy supply and situates our analysis at
the level of satisfied needs’’. Serrenho et al. [12] recent work on
useful work intensity supports this assertion. Meanwhile, Warr
and Ayres [13], Santos et al. [14] and Guevara et al. [15], all found
empirical evidence suggesting useful work is a better candidate as
0306-2619/Ó2015 The Authors. Published by Elsevier Ltd.
This is an open access article under the CC BY license (
Corresponding author.
E-mail address: (P.E. Brockway).
Applied Energy 155 (2015) 892–903
Contents lists available at ScienceDirect
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a factor of production (than primary energy) to explain economic
growth. This gets us to the crux of our argument: if it is useful work
and not primary energy that supplies economic needs, then we
should conduct energy and economic analyses at that level.
The few published time-series studies of useful work account-
ing have focussed largely on industrialised countries including
the US, UK and Japan (e.g. [16–18] and later all EU-15 countries
[12]. Somewhat curiously, these country-scale analyses typically
focus on economic implications and linkages, rather than
energy-based conclusions. Brockway et al. [19] set out to address
this imbalance, by undertaking a 50 year time-series analysis
(1960–2010) of the US and UK. They found the US and UK may
no longer be increasing their aggregate exergy efficiency, as
increases in process level efficiencies are offset by efficiency dilu-
tion taking place [19], following the case of Japan [17]. In short:
individual technology gains in efficiency are being overtaken by
using increasing amounts of less efficient processes, such as
air-conditioning. This raises the question: could the same be hap-
pening in China?
Numerous Extended Exergy Accounting (EEA) studies have been
published on China (e.g. [20–32]). EEA is a biophysical exergy anal-
ysis method, developed largely by Wall and Sciubba in the 1990s
(e.g. [33–35]) to examine the embedded exergy of all natural
resources inputs (e.g. energy, natural materials) and associated
outputs of the economy (e.g. food, materials, wastes). This valuable
technique helps understand societal exergy consumption. It is
complementary to the useful work accounting method applied
here, which is based on an ‘‘energy carriers for energy use’’
approach [36] introduced at a national-scale by Reistad [37] in
1975, which examines the exergy destruction of energy conversion
processes from primary exergy to end useful work. The key distinc-
tion is that EEA is akin to a mass-balance analysis (except it studies
exergy content not mass) whereas Reistad’s approach estimates
the thermodynamic work done by the energy system to deliver
energy services. It is the latter approach we require for detailed
energy system analysis – and such national-scale useful work
accounting studies for China are rare (e.g. [38]), and none to date
examine a long time-series.
To address the lack of exergy-based analyses in China which
examine time-series results through an energy demand lens, we
pose the following research question: What new insights can useful
work analysis provide for historical and future energy demand in
China? In response, we provide three novel, linked analyses. To
start, we undertake the first historical exergy efficiency and useful
work analysis for China, covering the period 1971–2010. Next, we
adopt an index decomposition analysis to identify the key drivers
of change in China’s useful work. Last, we develop a useful work
based method for projecting China’s primary energy demand to
2030, and also test implications of potential future declines in
the rate of exergy efficiency improvement.
The paper proceeds as follows. After the Introduction, Section 2
contains Methods and Data, Results and Discussions are in
Section 3, with Conclusions in Section 4.
2. Methods and data
2.1. Historical useful work analysis (1971–2010)
2.1.1. Method Summary
Reistad [37] defined exergy as ‘available energy’. As depicted
in Fig. 1, at a country-scale, primary exergy of energy carriers
(e.g. coal, oil, gas, renewables, food and feed) is transformed into
ready to use ‘final energy’ (e.g., diesel or electricity), which is
then used to provide ‘useful work’ (i.e. through heat, mechanical
drive, manual labour or electrical devices), to ultimately provide
energy services (e.g. warmth, light, cooling, sustenance).
Carnahan et al. [39] defined task-level ‘useful work’ ðU
‘‘the minimum exergy input to achieve that task work transfer’’.
For our purposes, task-level means sub-class (j) (e.g. diesel road
transport or low temperature heat) levels nesting within overall
main classes (i) of energy use (i.e. heat, muscle work, transport,
mechanical drive). Task-level exergy efficiency ð
the second law thermodynamic efficiency of the energy conver-
sion from primary exergy to end useful work, defined by
Carnahan et al. [39] as:
Primary exergy values at task-level ðE
Þare then multiplied
with their associated task-level exergy efficiencies (
) to give an
estimate for task-level useful work ðU
Þ. When summed, we derive
an overall estimate for the total national-scale useful work
Þvia Eq. (2). Finally, national exergy (second law) effi-
ciency (
) is given by Eq. (3), which – following Carnahan et al.
[39] – we adopt as a country-scale measure of energy efficiency,
and use it as a term throughout this paper for consistency. Eq.
(2) also reveals the obvious (but important, as we see later) obser-
vation that useful work changes are supplied by changes in pri-
mary exergy and/or exergy efficiency.
Fig. 1. Conceptual diagram of primary exergy to useful work.
¼Useful work;U
Primary Exergy;E
¼The minimum exergy input to achieve that task work transfer
Maximum amount of reversible work done as system reaches equlibrium ð1Þ
P.E. Brockway et al. / Applied Energy 155 (2015) 892–903 893
Our country-scale useful work accounting approach builds on
the methodology developed by numerous authors including
Reistad [37], Wall [40], Ayres and Warr [41], and more recently
Serrenho et al. [42], who introduced a consistent International
Energy Agency (IEA) based input energy mapping framework.
Brockway et al. [19] made further advances to electricity applica-
tions and mechanical drive classes, which is also used in this study
for consistency and comparability. We apply these advances to
produce a first time-series analysis of China. Fig. 2 gives an over-
view of the basic stages:
2.1.2. Input data
Primary exergy inputs, E
;are first derived. IEA energy datasets
1971–2010 (1) for fossil fuel and biomass (combustible renew-
ables) provided much of the base data. IEA primary energy values
are converted to primary exergy inputs using chemical exergy
coefficients [43]. At an aggregate level, total primary exergy is
around 5% higher than the IEA’s Total Primary Energy Supply
(TPES) values. The inputs E
are then mapped to three main classes
(heat, mechanical drive and electricity) and to task-levels where
possible (e.g. Low Temperature Heat (LTH)), following recent
approaches [19,42]. The task-levels are listed in Appendix A.In
some cases, we extend the IEA end energy use breakdown to more
granular levels (e.g. road fuel split between transport modes) by
supplementing Chinese end consumption data in three key areas:
buildings [3,44–48]; transport [49–53]; and industry [54–58].
Next, task-level exergy efficiencies ð
Þfor transport, heat, and
electricity are added. Previous US–UK values [19] are modified by
Chinese data as follows. For transport, local fuel economy data
was used for road and rail [52,53,59,60]. For calculating Carnot effi-
ciencies (for heat exergy efficiencies), for external temperatures we
used 1971–2010 China monthly air temperature data [61], whilst
indoor temperatures (for LTH efficiencies) were weighted for
China’s city/rural split and assume a 20 year lag in comfort levels
versus UK data [62]. LTH first law efficiencies are based on Warr
et al. [18], Chen et al. [30] and Edwards et al. [63]. Steel and ammo-
nia industries are adopted (as with US–UK study) as representative
of High Temperature Heat (HTH) efficiencies, by virtue of having
the two highest proportions of Chinese industrial energy use
[58]. Process (GJ/tes) efficiency data for steel [54–57,64,65] and
ammonia (taken as 75% of UK values, based on average values from
Phylipsen et al. [65]) and the IEA [66] are combined with temper-
ature data to calculate time-series exergy efficiencies. For electric-
ity application efficiencies, values of 80% of those from the US–UK
analysis were typically used, based on evidence that China’s aver-
age devices were 10–20 years behind US–UK values across indus-
try, commerce and residential sectors [3,67].
Then, we calculated primary exergy and useful work values for
a fourth main class: muscle work. For human labour, estimates fol-
low Brockway et al. approach [68]: using manual labour popula-
tion [69,70], food intake data [71,72], and Smil’s estimated 13%
conversion efficiency of food to human useful work [73]. For
draught animals, we assumed 100 million draught animals in
China in 1990 [74], and a 1% annual decline in numbers from
1971 to 2010, mirroring India [75]. For animal useful work outputs,
we assumed 400 W average power output for a 5 h working day
over 120 working days/year, based on published data [74,76,77].
Estimates of intake feed requirements were based on
Ramaswamy and Krausmann [74,78].
Last, a note on data quality. For input energy data, two system-
atic discrepancies mean our national-level datasets underestimate
actual primary energy use. First, at a national-scale, IEA-based TPES
values are 5% lower than those of Lawrence Berkeley National
Laboratory (LBNL) China Energy Databook [46]. Second, reported
aggregate primary energy consumption in China is 10% higher
from aggregated regional versus national datasets [79]. However,
these differences are expected to be systematic, and thus have lim-
ited overall effect for our trends analysis. For task-level efficiencies,
whilst the China data sources are weaker in many instances than
the previous US–UK studies [19], overall trends and comparison
to US–UK results remain valid.
2.1.3. Useful work accounting outputs
Appendix A shows the task-level outputs of useful work, pri-
mary exergy and exergy efficiency. This data serves as task-level
inputs to the Logarithmic Mean Divisia Index (LMDI) decomposi-
tion analysis, or is summed to give useful work or exergy efficien-
cies at main class level (i.e. heat, mechanical drive, electricity and
muscle work) and country-scale.
2.2. LMDI decomposition (1971–2010)
LMDI decomposition is now the mainstream Index
Decomposition Analysis (IDA) technique for analysing drivers of
changes in CO
emissions (e.g. [80,81]) and sectoral energy use
such as manufacturing and transport (e.g. [82,83]). Using the
LMDI approach, we develop a new approach to reveal the relative
contribution of energy and efficiency drivers to China’s historical
useful work (U). First, we expand Eq. (2) (U=PE
) to yield Eq.
(4), which is based on task-level useful work (U
) and primary
exergy (E
), enabling the historical results to act as the input data
for the LMDI analysis. Eqs. (5)–(9) give the four drivers of useful
work changes: Input Exergy (D
); Main class structure (D
IEA Primary Energy data
(fossil fuels and biomass)
IEA Primary energy mapping
to task-level end uses
Food and feed data
Exergy chemical equivalent
conversion values
Primary exergy mapping to
Useful Work, Eij
Local country energy data
(e.g. electricity end use)
Task level exergy
Useful Work
results, U ij
Fig. 2. Useful work analysis flowchart.
894 P.E. Brockway et al. / Applied Energy 155 (2015) 892–903
sub-class (i.e. task) level structural change (D
); and task-level
efficiency (D
). This shows how LMDI decomposition can be used
to breakdown the overall exergy efficiency changes (from the main
analysis results in Section 3.1) into three parts (D
¼exp X
ln X
! ! ð6Þ
¼exp X
ln S
! ! ð7Þ
¼exp X
ln L
! ! ð8Þ
¼exp X
ln F
! ! ð9Þ
Þ=ðln U
ln U
Þ=ðln U
ln U
! ð10Þ
E= Primary exergy input to economy
= Main class exergy input
= Task-level exergy input
= Task-level useful work output
= log mean weighting function
X= Exergy input
S= Main class share of exergy input (E
L= Task-level share of exergy input within main class (E
F= Task-level exergy efficiency(U
= change in overall exergy input (E)
= change in share of exergy inputs between main classes (E
= change in task-level shares (E
) of exergy inputs within
each main class
2.3. China energy demand scenarios 2010–2030
After conducting the historical and decomposition analyses, we
develop and trial a new useful work-based methodology to esti-
mate primary energy demand to 2030, based on projections of
GDP and extrapolations of task-level exergy efficiencies under
illustrative constant and declining exergy efficiency growth rate
scenarios. Four steps were required. The first estimates China’s
useful work requirement for 2010–2030. To do this, 1971–2010
overall useful work energy intensity (UW/GDP) – calculated from
historical GDP data [84] – is extrapolated using a best-fitting curve
to 2030. Using World Bank forecasts of GDP for 2011–2030 [85]
see also the Supplementary Information – Section S1, China’s total
useful work (to deliver that GDP) in 2030 is then estimated.
Second, total projected useful work to 2030 is allocated to
task-levels. To start, useful work proportions from main classes
are estimated based on historic trend comparison in UK, US and
China. China and US allocations are shown in Fig. 3. Then, task level
allocations are derived, also based on comparisons to previous
US-UK values, which place China as 40 years behind US–UK allo-
cations. These results at task-level are given in the Supplementary
Information – Section S2.
Third, task-level exergy efficiencies are projected to 2030 under
two illustrative scenarios which have different efficiency gains
assumptions. In Scenario 1 (constant efficiency gains), China’s
1990–2010 task-level exergy efficiency changes are extended to
2010–2030. Typically this places China’s task-level efficiencies in
2030 as those of average US-UK values in 2010. In Scenario 2
(declining efficiency gains), only half of China’s 1990–2010 effi-
ciency gains are extended to 2010–2030, with two thirds of these
reduced gains assumed to occur in 2010–2020. There is some jus-
tification for the declining gains scenario, as Brockway et al. [19]
found that efficiency gains in important task-levels (e.g. residential
electricity and LTH) slowed or reversed in 1990–2010 (versus
1970–1990). Assuming an average 20 year lag for China, this could
mean similar effects exhibited in China by 2030. More detailed effi-
ciency results at task-level are given in the Supplementary
Information – Section S3. Whilst other efficiency scenarios are pos-
sible (and indeed probable), our two selected cases are intended to
represent the possible envelope of task-level efficiencies for 2010–
2030, and are thus valid to study the effects of declining efficiency
Fourth, estimates of total primary energy demand for 2010–
2030 are made at task-level (Eq. (11)) and aggregate level (Eq.
(12)). Suffix 1 and 2 refer to Scenario 1 and 2. Finally, the chemical
exergy conversion ratios [43] are removed to reveal primary
Fig. 3. China (1971–2030) and US (1860–2010) useful work allocations.
P.E. Brockway et al. / Applied Energy 155 (2015) 892–903 895
energy (i.e. TPES) projections to 2030 under these two scenarios,
with differences suggesting impacts of declining exergy efficiency
gains on primary energy demand.
3. Results and discussion
3.1. 1971–2010. useful work accounting results
Table 1 summarises useful work, primary exergy and exergy
efficiency results for 1971–2010, with task-level results given in
Appendix A for 1971 and 2010. China’s end useful work has
increased 10-fold since 1971, with electricity applications and
HTH industrial uses growing from 30% to 53% of total useful work.
Conversely, muscle work and low temperature heat have together
declined from 40% of total useful work to 8%.
Aggregate exergy efficiency has grown almost linearly from
5.3% to 12.5%. Table 1 (together with Appendix A) suggest a key
factor is the structural shift from (low efficiency) muscle work
and low temperature heat (20 °C) to (high efficiency) HTH. Fig. 4
illustrates a second reason: the strong growth in mechanical drive
and heat class efficiencies – which make up over half of total pri-
mary exergy inputs. The question of whether this linear aggregate
efficiency trend can continue is considered via the future scenario
analysis in Section 3.3.
Fig. 4 also compares China’s aggregate efficiency growth to the
stable US (10–11%) values from the previous US-UK study [19].
China’s exergy efficiency overtakes the US by around 2004. At first,
it is tempting to see China’s overtaking of the US’s aggregate effi-
ciency as ‘technological leapfrogging’ (e.g. [86]) – i.e. rapidly adopt-
ing high-efficiency technologies without having to deal with the
legacy of past low efficiency capital stock. In fact this is not the
case, since task-level exergy efficiencies are generally lower than
the US (except mechanical drive, which is a small component of
China’s energy use). This result implies structural differences make
a significant contribution to China’s increasing efficiency: i.e. its
production-focused industrial economy uses more high tempera-
ture heat and industrial processes versus the US’s mature con-
sumer economy. The index decomposition results in Section 3.2
support this view. In turn, this implies as China’s economy also
matures and its structure shifts towards that of the US, that this
may have a diluting effect on future overall exergy efficiency, as
seen later in Section 3.3.
Table 1
Summary of useful work analysis results 1971–2010.
Useful work analysis: output category 1971 1980 1990 2000 2010
Main category end use PJ % of total PJ % of total PJ % of total PJ % of total PJ % of total
Useful work
Direct heat 1087 71 1848 71 2787 68 3901 59 7602 51
Mechanical Drive 126 8 268 10 477 12 1140 17 2633 18
Electricity end uses 154 10 343 13 684 17 1460 22 4665 31
Muscle work 157 10 151 6 148 4 137 2 127 1
Total 1524 100 2610 100 4096 100 6638 100 15027 100
Primary exergy
Direct heat 15370 54 21560 56 28271 55 31504 49 51983 43
Mechanical Drive 1090 4 1971 5 2988 6 5625 9 12720 11
Electricity end uses 1592 6 3519 9 6283 12 13951 22 42507 35
Muscle work 10661 37 11760 30 13489 26 13398 21 13159 11
Total 28713 100 38810 100 51032 100 64478 100 120,369 100
Exergy efficiency (useful work/primary exergy)
Main category end use % efficiency % efficiency % efficiency % efficiency % efficiency
Direct heat 7.1 8.6 9.9 12.4 14.6
Mechanical Drive 11.6 13.6 16.0 20.3 20.7
Electricity end uses 9.7 9.7 10.9 10.5 11.0
Muscle work 1.5 1.3 1.1 1.0 1.0
Total 5.3 6.7 8.0 10.3 12.5
Fig. 4. China’s exergy efficiency by end use 1971–2010, compared to US aggregate efficiency.
896 P.E. Brockway et al. / Applied Energy 155 (2015) 892–903
Few comparative estimates are available of aggregate Chinese
efficiencies. Chen and Chen [30] calculate a value of 20%, twice that
of our 10% value for China in 2000. The main reasons are due their
exclusion of muscle work, and higher industry efficiency (e.g. 78%
for the chemical sector). Nakicenovic [87] estimated reforming
countries (e.g. China) exergy efficiencies in 1990 to be 10%, of a
similar order to our 8% estimate for 1990.
Fig. 5 shows how China’s 10-fold useful work growth was sup-
plied by a 4-fold increase in primary energy coupled to a 2.5-fold
gain in aggregate exergy efficiency: from 5% to 12.5%. In other
words, if China’s exergy efficiency had stayed at 5%, a 10-fold gain
in primary exergy would have been required to achieve the same
useful work supply level.
Finally, to understand the overall flow of exergy to end useful
work, and the exergy losses that occur during the various conver-
sion processes, useful work-based Sankey diagrams of China are
constructed for 1971 and 2010, as shown in Appendix B. They
show the transformation of China in 40 years from a largely agri-
cultural to industrial economy. By 2010, China is dominated by
energy dense fossil fuel inputs (versus food and feed for muscle
work) and energy intensive end uses, particularly in industry,
which underpins the rise in overall exergy efficiency.
3.2. LMDI decomposition results 1971–2010
The multiplicative factors are summarised in Table 2 for the
period 1971–2010, comparing three countries: China, the UK and
US. For China, the largest contribution to useful work growth is pri-
mary exergy, confirming the result of Fig. 5 Importantly, the overall
efficiency gain factor (2.5) is now split into three parts. First, the
main class structural change (1.39) tracks the move from less effi-
cient (i.e. muscle work) to more efficient (i.e. heat) main classes.
Second, we find sub-class structural change (1.19) is above 1.00,
which means that within each main class there has also been an
efficiency ‘concentration’ effect. (In contrast note the efficiency
‘dilution’ values of 0.87–0.88 for the US and UK). This is due to
China’s transition from agricultural society to industrial powerhouse,
causing structural shifts within main classes from lower to higher
efficiency categories (e.g. LTH to HTH). Third, task-level efficiency
gains (1.48) are the largest of the three efficiency gain factors.
The value of using the LMDI approach is also highlighted by
Table 2. Firstly, it confirms and quantifies the assertion stated in
Section 3.2: that overall structural change (1.66) is at least as
important to overall efficiency gains as task-level efficiency gains
(1.48). Secondly, we can directly compare factors to other coun-
tries. In this case, we see that China has not reached the point of
efficiency ‘dilution’ that can be seen in the US and UK – where
would be below 1.00 – as found earlier by Williams et al.
[17] for Japan. China’s improvements to task-level efficiencies
(1.48) are similar to US (1.29) and UK (1.58) values, confirming that
instead of technological leapfrogging, it is overall structural change
(1.66 for China versus 0.90 for US and UK) that has been responsi-
ble for China’s rise in overall aggregate efficiency to overtake the
3.3. Future exergy efficiency: impacts on primary energy projections
3.3.1. Step 1 – Useful work projection to 2030
China’s useful work and primary energy intensities (of eco-
nomic activity) are shown in Fig. 6, based on constant price GDP.
It shows a 66% reduction in useful work intensity from 12.0
(GJ/2005$US) in 1971 to 3.9 (GJ/2005$US) in 2010, compared to
an 86% reduction in primary energy intensity (210.7 to
29.8 GJ/2005$US) the standard metric for energy intensity (e.g.
[88]) – over the same period. The greater stability of useful work
intensity suggests useful work is more closely linked to GDP than
primary energy – supporting the key assumption noted earlier.
Useful work and primary energy intensities are projected to 2030
using best-fitting trendlines as shown in Fig. 6.
The World Bank’s GDP forecast for China in 2030 [85] is
$13.5 Trillion(US2005), a 3.5-fold increase from the
$3.8 Trillion(US2005) value in 2010. Using the useful work inten-
sity projection of 2.45 (GJ/US$2005) for 2030, this gives a useful
work estimate of 33.1EJ in 2030 (just over double the 15.0EJ con-
sumed in 2010) – to deliver that level of GDP.
3.3.2. Step 2 – Allocation of task-level useful work
Fig. 7 shows the projected annual useful work growth to 2030 is
almost linearly 27–28 Mtoe/year. This is due to two effects can-
celling each other out: a slowdown in GDP growth mirroring useful
work intensity reductions. At a main class level, as China’s econ-
omy matures, a slowdown in heat’s contribution to useful work
is offset by growth in electricity and mechanical drive (mainly
transport) classes. This appears broadly consistent with other eco-
nomic forecasts for China used in energy modelling (e.g. [6]).
Table 2
Summary of LMDI decomposition factors 1971–2010 for China, US, UK.
Country U Dex Dstr Ddil Deff
Main class
China 9.76 3.96 1.39 1.19 1.48
US 1.53 1.32 1.03 0.88 1.29
UK 1.43 1.01 1.04 0.87 1.58
Country UD
Overall structural change Task-level
China 9.76 3.96 1.66 1.48
US 1.53 1.32 0.90 1.29
UK 1.43 1.01 0.90 1.58
Fig. 5. China 1971–2010 useful work analysis results vs 1971 datum.
P.E. Brockway et al. / Applied Energy 155 (2015) 892–903 897
3.3.3. Step 3 – Task-level exergy efficiencies
Next, task-level exergy efficiencies are projected based on the
linear and declining gains scenarios described earlier – see
Supplementary Information. The results at main class level are
shown in Fig. 8. In Scenario 1, stable gains in task-level exergy effi-
ciencies are combined with structural change in China in 2011–
2030 – moving towards a more service sector-based economy,
with associated decreases in higher efficiency processes (e.g. high
temperature heat) and increases in low-efficiency activities (e.g.
residential and commercial electricity), as shown earlier in Fig. 3.
This results in only a small increase in national aggregate exergy
efficiency from 12.5% to 13.0% in 2030. The green wedge in Fig. 8
illustrates the effect of this structural change, compared to a sim-
ple extrapolation of China’s 1990–2010 aggregate efficiency, which
would result in aggregate exergy efficiency of around 17% in 2030.
In Scenario 2, which includes both structural change and slowing
of task-level efficiency gains, aggregate exergy efficiency peaks at
12.8% before 2025, then reduces to 12.5% by 2030. Therefore most
of the reduction in overall efficiency is due to assumed structural
change than the difference in task-level efficiencies under the
two scenarios.
For heat and mechanical drive classes, the projected efficiency
dilution is so strong (i.e. less industrial usage and more consumer/
commercial use), their efficiencies decline by 2030 under both sce-
narios. As electricity provides an increasing share of useful work by
2030, this accelerates the slowdown (scenario 1) and decline (sce-
nario 2) in overall exergy efficiency. Mechanical drive efficiency
stagnates in this analysis under both scenarios, since it balances
task-level efficiencies that were increasing (e.g. static motors and
aviation) and decreasing (e.g. road transport – due to more
Primary Energy Intensity (GJ/US$2005)
Fig. 6. Comparison of China primary energy and useful work intensities.
Fig. 7. China – useful work projection to 2030.
Efficiency scenario differences
Structural change
Fig. 8. China – exergy efficiency scenario results.
898 P.E. Brockway et al. / Applied Energy 155 (2015) 892–903
cars/less motorcycles, and more heavy duty-trucks). However, as
the smallest of the three main classes, this effect has limited
impact on the aggregate exergy efficiency.
3.3.4. Step 4 – Primary end demand in 2030
Finally, the useful work-based primary energy estimates are cal-
culated based on the assumed efficiency scenarios. The results are
compared in Fig. 9 to five published reference (i.e. current policies)
scenarios [67,89–92] and a top-down primary energy intensity
(TPES/GDP) based estimate (derived econometrically via the
best-fit TPES/GDP projection shown earlier in Fig. 6). By 2030, our
Scenario 1 (6000 Mtoe/year) requires 900 Mtoe/year more primary
energy than the econometric estimate, whilst Scenario 2 – due to
assumed declining efficiency gains – requires an additional
300 Mtoe/year (compared to Scenario 1). The TPES/GDP derived pri-
mary energy estimate (as with the other five reference projections)
slows over time, following the assumed slow-down in GDP growth.
In contrast, our useful work derived projections show more linear
increases, as with flat overall exergy efficiencies (shown earlier in
Fig. 8), the linear projected growth in useful work required (see ear-
lier Fig. 7) is passed on to required primary energy inputs.
Our useful work-based projections are significantly higher than
the five reference cases. The three reference scenarios using a 2010
base year [89–91] produce estimates of 4300–5000 Mtoe/year in
2030, whilst the two scenarios with a 2005 base year [67,92] esti-
mate primary energy consumption as 3200 Mtoe in 2030. A key
aspect therefore appears the choice of base year, with the 2005
base year models missing China’s step up in energy consumption,
and so undercut the projections of later base year models.
Perhaps this illustrates how tricky energy forecasting is, as Smil
[93] notes: ‘‘long-range energy forecasters have missed every
important shift of the past 2 generations..[and they]..will continue
to be wrong’’.
Nevertheless, the fact remains the traditional energy models
give lower estimates of primary energy than our simple useful
work-based approach – so it’s worth reflecting on this. Most
importantly, we base our projections on a different energy inten-
sity metric versus mainstream models – ours is based on useful
work (U/GDP), as this measures the energy level delivered to eco-
nomic activities, rather than on primary energy (TPES/GDP) enter-
ing the economy. Moreover, our TPES/GDP based projection is 20%
below our U/GDP based projections – showing that this distinction
is an important one. The GDP projections that we use are consis-
tent with other models (e.g. [6]). Our methodology is also
top-down: it starts from an aggregate demand estimation, and
then builds up its constituent elements from task-share trends.
Other energy models tend to be bottom-up, using demand and
technology trends of various sectors. We attach more detailed sce-
nario data in the Supplementary Information.
Whilst we believe the useful work based approach to primary
energy forecasting is justified by the observed links between
aggregate economic activity and useful work, significant caveats
exist around the accuracy of the underlying data to our energy pro-
jection conclusions. For the useful work calculations for 1971–
2010, though the primary exergy data is relatively robust (relying
mainly on IEA energy balance data), the task-level efficiencies have
greater uncertainty, being based on often partial data. In turn, pro-
jecting task-level useful work allocations and exergy efficiencies to
2030 amplifies any data inaccuracies. However the driving ratio-
nale of the paper was to develop a new technique based on useful
work. The result highlights the possible importance of this method
and thus mandate for further study.
4. Conclusions
To address the lack of time-series exergy analyses for China
which examine energy demand drivers and implications, we set
the following research question: What new insights can useful work
analysis provide for historical and future energy demand in China?
First, our historical analysis found China’s exergy efficiency grew
linearly from 5.3% (1971) to an impressive 12.5% (2010), placing
it between the US (11%) and the UK (15%). In addition, a striking
10-fold rise in China’s useful work occurred from 1971 to 2010,
supplied by a 4-fold increase in primary exergy and a 2.5-fold
increase in exergy efficiency. Second, using LMDI decomposition
we found efficiency growth was split evenly between task-level
efficiency gains and structural change (e.g. moving from muscle
work to mechanical drive). Third, a new useful work-based energy
forecasting technique is developed and trialled, which – based on
two illustrative exergy efficiency scenarios – projects China’s
2030 primary energy demand in the range of 6000–6300 Mtoe, sig-
nificantly higher than the 4500–5200 Mtoe estimates from pub-
lished sources using traditional energy models which use the
same 2010 baseline year.
The results allow several key insights. Firstly, if China’s exergy
efficiency had stayed at 5%, a 10-fold (rather than 4-fold) gain in pri-
mary exergy would have been required to achieve the same useful
work supply level. Through the mechanism of the macro-economic
rebound effect, however, as Ayres et al. [94] and Schipper and
Grubb [95] established, lower efficiency gains may in fact translate
to lower economic growth, and hence lower required useful work.
Second, the application of LMDI decomposition to useful work
results provided robust insights: revealing China’s efficiency rise
above the US was not due to technological leapfrogging, but greater
use of energy intensive (yet more exergy efficient) industrial pro-
cesses. Third, in common with the US and UK, China may approach
an asymptotic exergy efficiency maximum by 2030, as its economy
matures and efficiency dilution starts. Such dilution is already
Fig. 9. China – primary energy (TPES) forecasts to 2030.
P.E. Brockway et al. / Applied Energy 155 (2015) 892–903 899
forecast: the modal shift to cars [67] will reduce mechanical drive
exergy efficiency; a rapid increase in residential electricity [3];
and a peaking in the share of HTH allied to a shift to greater residen-
tial LTH. Fourth, our extension of useful work based technique pro-
jects higher primary energy demand in China by 2030 versus
traditional bottom-up energy model estimates (i.e. based on pri-
mary or final energy). Further studies investigating the possible
reasons (e.g. differences in assumed future energy efficiency sav-
ings, structural consumption, energy rebound and efficiency dilu-
tion) would therefore be beneficial.
Overall, the useful work method appears a valuable technique to
give new insights into Chinese energy consumption and efficiency –
past, present and future. Given the implications to future energy
demand and associated policies, further research is encouraged.
First, work to improve the consistency of the useful work method
would be of benefit – such as the treatment of renewables,
non-energy use, active/passive system efficiencies, or extending the
analysis boundary to include energy services, as others suggest
[38,87,96]. Second, contrast the construction of traditional (primary
and final energy) versus useful work energy models, to uncover the
reasons for energy projection differences. Third, undertake further
economic analysis to test the key assumption underpinning this
work: that useful work is a more suitable parameter for energy and
economic analysis than primary energy. Lastly, policy implications
could be explored – such as how to meet higher (than expected) pri-
mary energy demand, or how to amend micro-efficiency policies to
capture energy savings before rebound occurs.
We gratefully acknowledge the support of Engineering and
Physical Sciences Research Council (EPSRC) and Arup for contribut-
ing to the PhD CASE (Collaborative Award in Science and
Engineering) scholarship of the first author. The support of the
Economic and Social Research Council (ESRC) is also gratefully
acknowledged. The work contributes to the programme of the
ESRC Centre for Climate Change Economics and Policy. Also, we
thank João de Santos and Tiago Domingos of Instituto Superior
Técnico, Lisbon, for sharing their unpublished work. We would like
to thank the two anonymous reviewers and the editor of Applied
Energy for their useful suggestions and valuable comments.
Appendix A. Useful work accounting outputs: China – 1971,
(see Table A1)
Table A1
Useful work accounting outputs: China – 1971, 2010.
Main class, iTask level, j1971 2010
PJ PJ (%) PJ PJ (%)
Heat LTH (Low Temperature Heating 20 °C) 435 10,103 4.3 973 20,281 4.8
MTH1 (Medium Temperature Heating 100 °C) 30 247 12.1 592 4428 13.4
MTH2 (Medium Temperature Heating 200 °C) 301 2657 11.3 1913 10,506 18.2
HTH (High Temperature Heating 600 °C) 283 2362 12.0 3963 16,695 23.7
Sub total 1049 15,370 6.8 7441 51,910 14.3
Mechanical drive – Gas/diesel oil (assume diesel road
20 114 17.3 821 3728 22.0
Mechanical drive – Domestic Aviation fuel, jet fuel 0 0 n/a 158 642 24.6
Mechanical drive – Gasoline fuel (Petrol road vehicles 42 242 17.3 691 3920 17.6
Mechanical drive – Diesel/gas oil fuel (Boat engines) 2 17 13.0 188 915 20.5
Mechanical drive – Industry static motors (diesel
28 118 23.5 522 1934 27.0
Mechanical drive – Gas/diesel fuel (diesel trains) 1 8 13.0 64 310 20.5
Mechanical drive – Gas/diesel fuel (tractors) 26 255 10.2 88 799 11.0
Mechanical Drive – bio-diesel/bio-gasoline (road
0 0 n/a 16 80 n/a
Mechanical drive – bio-diesel/bio-gasoline (road
0 0 n/a 0 0 n/a
Mechanical drive – Gas/diesel oil (assume diesel cars) 0 1 20.4 85 385 22.0
Mechanical drive – Gas fired engines (for pipeline
0 0 n/a 2 7 n/a
Mechanical drive – Coal (steam powered trains) 7 333 2.2 0 0 n/a
Mechanical drive – Coal (steam powered boats) 0 0 n/a 0 0 n/a
Mechanical drive sub-total 126 1090 11.6 2633 12,720 20.7
Electricity Lighting 1 84 0.8 46 2836 1.6
Domestic/commercial – Space heating 0 31 1.3 57 3303 1.7
Domestic – Hot water/cooking 1 21 3.0 47 1272 3.7
Industry – HTH process heating 16 199 7.9 443 4537 9.8
Electrolytic end use – Industry 11 153 7.5 370 3490 10.6
Communications/electric devices 0 0 0.1 1 267 0.3
Refrigeration/air conditioning 4 307 1.4 144 8221 1.7
Domestic – Wet/dry motor driven appliances 0 0 10.0 17 138 12.6
Other mechanical drive motors 123 797 15.4 3537 18,442 19.2
Electricity – Sub-total 156 1592 9.8 4662 42507 11.0
Muscle work Human 26 5432 0.5 38 9626 0.5
Draught animals 131 5229 2.5 88 3533 2.5
Muscle work – Sub-total 157 10,661 1.5 127 13,159 1.0
Total Grand total 1488 28,713 5.2 14,863 120,296 12.4
900 P.E. Brockway et al. / Applied Energy 155 (2015) 892–903
Fig. B1. China E-Sankey Diagram (1971).
Fig. B2. China E-Sankey Diagram (2010).
Appendix B. Primary exergy to useful work E-Sankey flowmaps: China – 1971 and 2010
(see Figs. B1 and B2)
P.E. Brockway et al. / Applied Energy 155 (2015) 892–903 901
Appendix C. Supplementary material
Supplementary data associated with this article can be found, in
the online version, at
Appendix D. Data Statement. Supplementary material
A complete results file, produced following the methodology
and sources described in this paper, has been deposited at the
University of Leeds Data Repository at
[1] International Energy Agency (IEA). Extended world energy balances, IEA World
energy statistics and balances (database); 2013. <http://www.oecd-ilibrary.
org/energy/ data/ie a-world-energy-statis tics-an d-balan ces/ext ended-world-
[2] International Energy Agency (IEA). Energy technology perspectives 2012:
Pathways to a clean energy system; 2012 <>
[3] Letschert VE, Mcneil MA, Zhou N. Residential electricity demand in China – can
efficiency reverse the growth? Lawrence Berkeley National Laboratory (LBNL);
[4] Energy Foundation China. Background report: vehicle fuel economy in china;
2001 <>.
[5] International Energy Agency (IEA). Energy technology perspectives 2010:
scenarios & strategies to 2050. IEA, France; 2010.
[6] International Energy Agency (IEA). World energy model documentation 2013
Version; 2013 <
[7] Organisation for Economic Co-operation and Development (OECD). Energy and
climate policy: bending the technological trajectory. OECD studies on
environmental innovation. OECD Publishing; 2012.
[8] Hammond GP. Industrial energy analysis, thermodynamics and sustainability.
Appl Energy 2007 Jul;84:675–700.
[9] Rosen MA, Dincer I, Kanoglu M. Role of exergy in increasing efficiency and
sustainability and reducing environmental impact. Energy Pol
[10] Groscurth HM, Kummel R, van Gool W. Thermodynamic limits to energy
optomisation. Energy 1989;14(5):241–58.
[11] Percebois J. Is the concept of energy intensity meaningful? Energy Econ 1979.
[12] Serrenho AC, Sousa T, Warr B, Ayres RU, Domingos T. Decomposition of useful
work intensity: the EU (European Union) – 15 countries from 1960 to 2009.
Energy 2014;76:704–15.
[13] Warr BS, Ayres RU. Evidence of causality between the quantity and quality of
energy consumption and economic growth. Energy Elsevier Ltd
[14] Santos J, Domingos T, Sousa T, Aubyn MS. Useful work & economic growth:
Portugal. TBC; 1960–2009.
[15] Guevara Z, Sousa T, Domingos T. An approach for a better evaluation of energy
efficiency trends using index decomposition analysis and useful work
accounting. Energy Econ 2014.
[16] Ayres RU, Warr B. Accounting for growth : the role of physical work. Struct
Chang Econ Dyn 2005;16:181–209.
[17] Williams E, Warr B, Ayres RU. Efficiency dilution: long-term exergy conversion
trends in Japan. Environ Sci Technol 2008;42(13):4964–70.
[18] Warr B, Ayres R, Eisenmenger N, Krausmann F, Schandl H. Energy use and
economic development: a comparative analysis of useful work supply in
Austria, Japan, the United Kingdom and the US during 100 years of economic
growth. Ecol Econ. Elsevier B.V 2010;69(10):1904–17.
[19] Brockway PE, Barrett JR, Foxon TJ, Steinberger JK. Divergence of trends in US
and UK aggregate exergy efficiencies 1960–2010. Environ Sci Technol
[20] Chen B, Chen GQ. Resource analysis of the Chinese society 1980–2002 based
on exergy—Part 4: Fishery and rangeland. Energy Pol 2007;35(4):2079–86.
[21] Chen GQ, Chen B. Extended-exergy analysis of the Chinese society. Energy.
Elsevier Ltd 2009;34(9):1127–44.
[22] Dai J, Chen B. Extended exergy-based ecological accounting of China during
2000–2007. Procedia Environ Sci 2011;5:87–95.
[23] Chen B, Chen GQ. Exergy analysis for resource conversion of the Chinese
Society 1993 under the material product system. Energy 2006;31(8–
[24] Dai J, Chen B, Sciubba E. Extended exergy based ecological accounting for the
transportation sector in China. Renew Sustain Energy Rev. Elsevier
[25] Dai J, Chen B, Sciubba E. Ecological accounting based on extended exergy: a
sustainability perspective. Env Resour Econ 2014;48:9826–33.
[26] Chen B, Dai J, Sciubba E. Ecological accounting for China based on extended
exergy. Renew Sustain Energy Rev. Elsevier; 2014.
[27] Dai J, Fath B, Chen B. Constructing a network of the social-economic
consumption system of China using extended exergy analysis. Renew
Sustain Energy Rev Elsevier 2012;16(7):4796–808.
[28] Dai J, Fath B, Chen B. Constructing a network of the social-economic
consumption system of China using extended exergy analysis. Renew
Sustain Energy Rev Elsevier 2012;16(7):4796–808.
[29] Ji X, Chen GQ. Exergy analysis of energy utilization in the transportation sector
in China. Energy Pol 2006;34(14):1709–19.
[30] Chen B, Chen GQ, Yang ZF. Exergy-based resource accounting for China. Ecol
Modell 2006;196(3–4):313–28.
[31] Chen B, Chen GQ. Modified ecological footprint accounting and analysis based
on embodied exergy — a case study of the Chinese society 1981–2001. Ecol
Econ 2006;61:355–76.
[32] Chen GQQ, Qi ZHH. Systems account of societal exergy utilization: China 2003.
Ecol Modell 2007 Nov;208(2–4):102–18.
[33] Wall G. Exergy conversion in the Japanese society. Energy 1990;15(5):435–44.
[34] Wall G, Sciubba E, Naso V. Exergy use in the Italian society. Energy
[35] Sciubba E. Beyond thermoeconomics? The concept of extended exergy
accounting and its application to the analysis and design of thermal
systems. Exergy Int J 2001 Jan;1(2):68–84.
[36] Ertesvag IS. Society exergy analysis : a comparison of different societies.
Energy 2001;26:253–70.
[37] Reistad G. Available energy conversion and utilization in the United States.
ASME Trans Ser J Eng Power 1975;97:429–34.
[38] Ma L, Allwood JM, Cullen JM, Li Z. The use of energy in China : tracing the flow
of energy from primary source to demand drivers. Energy Elsevier Ltd
[39] Carnahan W, Ford KW, Prosperetti A, et al. Technical aspects of the more
efficient utilization of energy: Chapter 2 – Second law efficiency: the role of
the second law of thermodynamics in assessing the efficiency of energy use.
Am Inst Phys Conf Ser 1975;25:25–51.
[40] Wall G. Exergy – A useful concept. PhD Thesis, Chalmers Univ Technol Univ
Göteborg; 1986 <>.
[41] Ayres RU, Ayres LW, Warr B. Exergy, power and work in the US economy,
1900–1998. Energy 2003;28(3):219–73.
[42] Serrenho AGCH. Useful Work as an Energy End-Use Accounting Method:
Historical and Economic Transitions and European patterns. PhD Thesis, Univ
Lisboa; 2014.
[43] Szargut J, Morris DR, Steward FR. Exergy analysis of thermal, chemical and
metallurgical processes. New York: Hemisphere; 1988.
[44] Amecke H, Deason J, Hobbs A, Novikova A, Xiu Y, Shengyuan Z. Buildings
energy efficiency in China, Germany, and the United States. San Francisco,
USA: Climate Policy Initiative; 2013.
[45] Murata A, Kondou Y, Hailin M, Weisheng Z. Electricity demand in the Chinese
urban household-sector. Appl Energy 2008;85:1113–25.
[46] Lawrence Berkley National Laboratory (LBNL). China Energy Databook v8.0;
2013. <>.
[47] Pachauri S, Jiang L. The household energy transition in India and China. Energy
Pol 2008 Nov;36(11):4022–35.
[48] Catania P. China’s rural energy system and management. Appl Energy 1999
[49] Hao H, Wang H, Ouyang M. Fuel consumption and life cycle GHG emissions by
China’s on-road trucks: Future trends through 2050 and evaluation of
mitigation measures. Energy Pol Elsevier 2012;43:244–51.
[50] Huo H, He K, Wang M, Yao Z. Vehicle technologies, fuel-economy policies, and
fuel-consumption rates of Chinese vehicles. Energy Pol Elsevier 2012;43:
[51] He K, Huo H, Zhang Q, et al. Oil consumption and CO
emissions in China’s road
transport: current status, future trends, and policy implications. Energy Pol
[52] Qunren L, Yushi M. China’s transportation and its energy use. Energy Sustain
Dev Int Energy Initiat Inc. 2001;5(4):92–9.
[53] Wang M, Huo H, Johnson L, He D. Projection of Chinese motor vehicle growth,
oil demand, and CO
emissions through 2050. US: Energy Systems Division,
Argonne National Laboratory; 2006.
[54] Hasanbeigi A, Price L, Aden N. A comparison of iron and steel production
energy use and energy intensity in China and the U.S.; 2011 <http://china.>.
[55] He F, Zhang Q, Lei J, Fu W, Xu X. Energy efficiency and productivity change of
China’s iron and steel industry Accounting for undesirable outputs. Energy Pol
Elsevier 2013;54:204–13.
[56] Price L, Sinton J, Worrell E, Phylipsen D, Xiulian H, Ji L. Energy use and carbon
dioxide emissions from steel production in China. Energy 2002;27:429–46.
[57] Hasanbeigi A, Jiang Z, Price L. Analysis of the past and future trends of energy
use in key medium- and large-sized Chinese steel enterprises, 2000–2030.
Berkeley, USA; 2013 <
[58] Hasanbeigi A, Price L, Fino-chen C, Lu H, Ke J. Retrospective and prospective
decomposition analysis of Chinese manufacturing energy use, 1995-2020;
2013 <>.
[59] Hao H, Wang H, Ouyang M. Fuel conservation and GHG (Greenhouse gas)
emissions mitigation scenarios for China’s passenger vehicle fleet. Energy
Elsevier Ltd 2011;36(11):6520–8.
[60] Teter J. China transport efficiency data. UC Davis data sent by email to author;
902 P.E. Brockway et al. / Applied Energy 155 (2015) 892–903
[61] National Climatic Data Centre. National climatic data centre – climate data
online: dataset discovery; 2014 <>.
[62] Department of Energy & Climate Change (DECC). Energy Consumption in the
UK: 2013; 2013 <
[63] Edwards RD, Smith KR, Zhang J, Ma Y. Implications of changes in household
stoves and fuel use in China. Energy Pol 2004;32:395–411.
[64] Ross M, Feng L. The energy efficiency of the steel industry of China. Energy
[65] Phylipsen D, Blok K, Worrell E, De BeerJ. Benchmarking the energy efficiency of
Dutch industry : an assessment of the expected effect on energy consumption
and CO
emissions. Energy Pol 2002;30:663–79.
[66] Saygin D, Patel M, Tam C, Gielen D. Chemical and petrochemical sector:
potential of best practice technology and other measures for improving energy
efficiency. IEA Inf Pap; 2009 <
[67] Fridley D, Zheng N, Zhou N, Ke J, Hasanbeigi A, Price L. China energy and
emissions paths to 2030 (2nd Edition). Lawrence Berkeley National Laboratory
(LBNL); 2012.
[68] Brockway PE, Barrett JR, Foxon TJ, Steinberger JK. Supporting information –
divergence of trends in US and UK aggregate exergy efficiencies 1960–2010.
Environ Sci Technol 2014;48:S1–S39.
[69] Laux R, Werner B, Knight I, Djerf K. Modernising China’s labour market
statistics. Labour Mark Trends April 2003;2003:203–11.
[70] Brooks R, Tao R. China’s labor market performance and challenges.
International Monetary Fund (IMF) working paper WP/03/210; 2003.
[71] Wirsenius S. Human Use of Land and Organic materials: Modeling the
Turnover of Biomass in the Global Food System [Internet]. PhD Thesis,
Chalmers Univ Technol; 2000 <
[72] Food and Agricultural Organisation of the United Nations (FAOSTAT). Food
supply kcal/day; 2013 <
[73] Smil V. Energy in world history. Boulder (Colorado, USA): Westview Press;
[74] Ramaswamy NS. Draught animals and welfare. Rev Sci Tech Off Int Epiz
[75] Down to Earth. 65 million draught animals; 2004 <http://www.downtoearth.>.
[76] Wilson RT. The environmental ecology of oxen used for draught power. Agric
Ecosyst Environ 2003;97:21–37.
[77] O’Neill DH, Kemp DC. A comparison of work outputs of draught oxen. J Agric
Engng Res 1989;43:33–44.
[78] Krausmann F, Erb K, Gingrich S, Lauk C, Haberl H. Global patterns of
socioeconomic biomass flows in the year 2000: a comprehensive assessment
of supply, consumption and constraints. Ecol Econ 2007;65:471–87.
[79] Guan D, Liu Z, Geng Y, Lindner S, Hubacek K. The gigatonne gap in China’s
carbon dioxide inventories – supporting information. Nat Clim Chang 2012.
[80] Wang C, Chen J, Zou J. Decomposition of energy-related CO
emission in China:
1957–2000. Energy 2005;30(1):73–83.
[81] Xu X, Zhao T, Liu N, Kang J. Changes of energy-related GHG emissions in China:
an empirical analysis from sectoral perspective. Appl Energy Elsevier Ltd
[82] Liu L-C, Fan Y, Wu G, Wei Y-M. Using LMDI method to analyze the change of
China’s industrial CO
emissions from final fuel use: an empirical analysis.
Energy Pol 2007 Nov;35(11):5892–900.
[83] Zhang M, Li H, Zhou M, Mu H. Decomposition analysis of energy consumption
in Chinese transportation sector. Appl Energy Elsevier Ltd
[84] World Bank. GDP (constant 2005 US$); 2014 <
[85] World Bank, The Development Research Center of State Council the People’s
Republic of China. China 2030: Building a Modern, Harmonious, and
Creative Society; 2012 <
[86] Goldemberg J. Viewpoint leapfrog energy technologies. Energy Pol
[87] Nakicenovic N, Gilli PV, Kurz R. Regional and global exergy and energy
efficiencies. Energy 1996;21(3):223–37.
[88] Liddle B. Revisiting world energy intensity convergence for regional
differences. Appl Energy Elsevier Ltd 2010;87(10):3218–25.
[89] International Energy Agency (IEA). World energy outlook 2013. Paris, France;
[90] US Energy Information Administration (USEIA). World primary energy
consumption. Int Energy Outlook 2014; 2014 [cited 2014 Nov 5] <http:// #release=IEO2013&subject=1-IEO2013&
[91] British Petroleum (BP) Ltd. World Energy Outlook 2035 – Excel Tables. World
Energy Outlook 2035; 2014 [cited 2014 Nov 5] <
[92] IIASA. MESSAGE LIMITS-base (RCP 8.5) Total Primary Energy Consumption
data. LIMITS Scenar. Database (version 1.0.0); 2014 [cited 2014 Nov 5]
[93] Smil V. Perils of long-range energy forecasting : reflections on looking far
ahead. Technol Forecast Soc Change 2000;65:251–64.
[94] Ayres RU, Turton H, Casten T. Energy efficiency, sustainability and economic
growth. Energy 2007;32:634–48.
[95] Schipper L, Grubb M. On the rebound? Feedback between energy intensities
and energy uses in IEA countries. Energy Pol 2000;28(6–7):367–88.
[96] Ayres RU. Eco-thermodynamics: economics and the second law. Ecol Econ
1998 Aug;26(2):189–209.
P.E. Brockway et al. / Applied Energy 155 (2015) 892–903 903
... The analysis of driving factors helps deeply understand the change mechanism of the aggregate exergy efficiency and provide further policymaking interventions. These limited papers mainly analyzed the end-use stage's driving factors [11][12][13], without considering the final service. Obviously, excessive attention to the end-use stage may overlook other stages' potential. ...
... On the other hand, recent papers focus more on the role of useful work in end energy use and economic activity [36], such as the evolution of useful energy or useful work intensity (useful work/gross domestic product). For example, Brockway et al. explored the trend of useful energy in the US and the UK from 1960 to 2010 [11] and China from 1971 to 2010 [13]. Serrenho et al. [12] However, what people need is not energy itself, but the "final services" it provides. ...
... Meanwhile, limited attention was paid to the analysis of driving factors, while most SEA-based studies concentrated on the evolution analysis of exergy efficiency. The analysis of driving factors helps us understand the energy efficiency evolution mechanism and provide further policy interventions; however, these papers often only analyzed the driving factors of the end-use stage [11][12][13]. For example, Serrenho et al. [12] analyzed the driving factors of thermodynamic efficiency improvement and the structural change of the end-use stage in fifteen EU countries from 1960 to 2009. ...
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Analysis of the change of overall energy efficiency performance of an energy system is a fundamental work for the energy-saving policymaking. However, previous studies seldom focus on energy stages from useful energy to final service, while most attention are paid on stages from energy source to useful energy. In this paper, we develop a high-resolution the Societal Exergy Analysis and Logarithmic Mean Divisia Index (SEA-LMDI) method to analysis changes and driving factors of the aggregate exergy efficiency, in which the boundary of the SEA is extended to passive systems and final services, and a LMDI decomposition method is developed to quantify contributions of efficiency factors and structure factors of all six stages on the aggregate exergy efficiency. A case study of China during 2005–2015 reveals that: (a) the aggregate exergy efficiency from energy source to final service is only from 3.7% to 4.8% during 2005–2015, showing a huge theoretical potential of efficiency improvement. (b) Large passive losses are identified in passive systems and nearly 2/3 of useful energy can be theoretically saved by improving passive systems. (c) Deep analysis of industrial coal-fired boilers indicates that the internal structural adjustments are also important for the aggregate improvement.
... China must create a sustainable energy development system with a high EROIPOU through energy structure adjustment. Brockway et al. [45] projected China's 2030 primary energy demand in the range of 6000-6300 mtoe, significantly higher than the 4500-5200 mtoe estimates from published sources using traditional energy models, because of useful work (minimum exergy input to achieve that task work transfer) not growing as quickly as primary exergy in China. Portion (a) indicates that the EROI POU of oil and natural gas are sensitive to energy price changes; portion (b) indicates that the EROI POU of coal, oil and natural gas is sensitive to URR changes. ...
... China must create a sustainable energy development system with a high EROI POU through energy structure adjustment. Brockway et al. [45] projected China's 2030 primary energy demand in the range of 6000-6300 mtoe, significantly higher than the 4500-5200 mtoe estimates from published sources using traditional energy models, because of useful work (minimum exergy input to achieve that task work transfer) not growing as quickly as primary exergy in China. Figure 11. ...
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There is a strong correlation between net energy yield (NEY) and energy return on investment (EROI). Although a few studies have researched the EROI at the extraction level in China, none have calculated the EROI at the point of use (EROIPOU). EROIPOU includes the entire energy conversion chain from extraction to point of use. To more comprehensively measure changes in the EROIPOU for China’s conventional fossil fuels, a “bottom-up” model to calculate EROIPOU was improved by extending the conventional calculation boundary from the wellhead to the point of use. To predict trends in the EROIPOU of fossil fuels in China, a dynamic function of the EROI was then used to projections future EROIPOU in this study. Results of this paper show that the EROIPOU of both coal (range of value: 14:1–9.2:1), oil (range of value: 8:1–3.5:1) and natural gas (range of value: 6.5:1–3.5:1) display downward trends during the next 15 years. Based on the results, the trends in the EROIPOU of China’s conventional fossil fuels will rapidly decrease in the future indicating that it is more difficult to obtain NEY from China’s conventional fossil fuels.
... Useful stage: relatively constant useful stage intensities and levelling off efficiency gains. The exergy economics literature finds that, conversely to final energy intensities, useful stage intensities have been remarkably constant over time, although most studies focus on EU countries [43][44][45] -note that Heun and Brockway also find constant intensities for Ghana [28], and that Guevara et al. find increasing intensities for Mexico [46]; the only known case of clearly decreasing useful exergy intensity so far being China [47]. Such results suggest that national economies may be, in most cases, more reliant on energy consumption that final stage studies suggest. ...
... The LMDI-I method is selected, as recommended by Ang [88,89] due to its simpler formulation as well as two mathematical properties: consistency in aggregation and perfect decomposition at the subcategory level. Following previous studies [28,47], the Spanish useful exergy supply is specified as a sum of different quantities: ...
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Given the climate change emergency, reducing energy consumption, which is responsible for most greenhouse gases emissions worldwide, is a priority. However, the strong historical link between energy consumption and economic growth questions whether continued economic growth is compatible with energy conservation targets. Conventional final energy analysis (common analysis methods applied at the final energy stage) has provided limited insights to this nexus. In response, this paper explores the extent to which useful stage analysis provides additional insights using three common methods: aggregate energy-economy analysis (growth rates, energy intensities, and Index Decomposition Analysis), energy-GDP causality testing, and Aggregate Production Function modelling, using Spain (1960–2016) as empirical case study. The results reveal that of the three methods investigated, aggregate energy-economy analysis provides the greatest insights, including that Spain is far from achieving absolute energy-GDP decoupling. Further, moving to the useful stage indicates that the extent of decoupling is even less than suggested at the final energy stage, and that increasing final energy consumption has historically fully offset efficiency gains. In contrast, whether applied at the final energy or useful stage, energy-GDP causality testing and Aggregate Production Function modelling reveal little about the energy-economy nexus — the results even suggest that these tools are not appropriate and may mislead. Thus, useful stage analysis is necessary but not sufficient for delivering further energy-economy insights; there is also a need for exploring alternative, reliable, energy-economy analysis methods. Indeed, the lack of robustness of Aggregate Production Function modelling and energy-GDP causality testing is worrisome.
... More recently, studies have adopted a time-series approach to investigate the energy systems of the UK looking at use of exergy in different end-uses and the link between exergy efficiency and energy demand reduction (Miller et al., 2016;Hardt et al., 2018). Similar work has been undertaken for China (Brockway et al., 2015), evaluating the change of exergy flows and efficiency of the economy between 1971 and 2010, Portugal (Serrenho et al., 2016) and Ghana (Heun and Brockway, 2019). ...
Industry is responsible for 40\% of global greenhouse gas (GHG) emissions, which drive anthropogenic climate change. It is also an important consumer of energy and materials, accounting for 40\% of final energy consumption. While other sectors such as power generation, heat and transport are expected to reduce emissions in the near future, industry is often harder to decarbonise. Efficiency in the use of both materials and energy can be a powerful tool for addressing a wide range of industrial emissions mechanisms, but these approaches are often pursued separately. Exergy analysis is a powerful assessment tool that can combine material and energy efficiency approaches into one, while also accounting for resource quality as well as quantity. This thesis investigates how exergy analysis can be applied in industrial sites to achieve real resource and emissions savings. Production of ammonia and syngas through the steam methane reforming (SMR) process are chosen as the case study, given the high production volume, resource consumption and emissions arising from this industry. This thesis develops a comprehensive exergy analysis methodology and applies it to a detailed ammonia plant simulation at various level from the site to the equipment-level. Specific modelling choices are made, to allow for disaggregation of exergy inefficiencies according to the mechanisms they are caused by. The approach considers multiple aspects such as exergy calculations, flow visualisation, efficiency definitions and other metrics. In addition, network theory is used to assess the ammonia site using network analysis metrics, taking into account the complex nature of the site. The results show that SMR is the main source of inefficiency with an exergy efficiency of 68\% and exergy destruction of 165 MW, followed by the ammonia synthesis and water gas shift plants. The SMR plant is then investigated in detail. The two main combustors and two heat exchangers are the highest contributors to exergy destruction. Overall, combustion and heat exchange are the main exergy destruction mechanisms with 57 and 39 MW respectively, while reactions are responsible for 11 MW. Total exergy efficiency definitions are found to consistently overestimate efficiency compared to transit and fuel-product definitions. Network analysis is shown to detect communities of tightly connected plants in the ammonia site. Network metrics rank areas of the site with eigenvector centrality found to be the most relevant metric, but additional tailoring is required to make them applicable to an industrial setting. Industrial decision-makers at the level of the site can have immediate impact on reducing emissions from industry. However, typical exergy analysis studies have been addressed primarily to process designers, based on simulated, static data. So the second step involved applying this methodology to real, dynamic data from an ammonia site. Two years of data from 311 at minute-level frequency are collected. This thesis develops boundary definition and data reconciliation methods for missing and un-metered data, consistent with preserving the dynamic nature of the plant data. The analysis finds average conventional and transit exergy efficiencies for the plant (71\%, 15\%) and its constituent processes: primary reformer (86\%, 40\%), secondary reformer (96\%, 71\%), high-temperature shift (HTS) (99.7\%, 77\%), combustor (56\%, 55\%) and heat exchange section (85\%, 82\%). Overall exergy loss and destruction is 80 MW; the primary reformer and combustor contribute 35 MW and 33 MW respectively. Efficiencies often fluctuate much lower than the maximum value attained and are consistently lower than in the simulation, presenting significant opportunity. The data-driven approach is then extended to evaluate long-standing arguments in favour of exergy such as its ability to evaluate real resource loss and to reduce emissions. Previous studies have focused on analytical, model-based approaches to compare emissions to exergy performance, while parametric studies on the influence of operating parameters on exergy efficiency have also relied on simulations. Different intensity, efficiency and performance metrics are evaluated for the plant and statistically analysed using correlation coefficients, univariate and multivariate first-order and second-order regression models. Exergy efficiency is found to correlate well with a number of performance indicators such as energy intensity. It is the best correlated performance metric to both carbon intensity and cost per tonne of product, quantitatively confirming a long-held qualitative assumption in literature. Exergy efficiency is better correlated with reduced emissions from combustion than with reduced process emissions, indicating its limitations in dealing with that mechanism of industrial emissions. The overall contribution of this thesis is to develop and apply an exergy analysis methodology to a real plant for the first time. It identifies important aspects of the analysis and outlines methods to overcome data collection and cleaning challenges in real sites. Finally, it assesses the ability of exergy efficiency to lead to real resource and emissions savings in industrial sites. Industry needs to decarbonise rapidly by 2050, if the worst effects of climate change are to be avoided. Exergy analysis proves a useful tool for assessing improvement opportunities in industrial sites and can aid towards that goal.
... al decoupling relative to GDP (seeand Krausmann et al., 2018 for example). Other researchers have calculated the long-term energy intensity and evaluated energy decoupling at the useful stage.Warr, Ayres, Eisenmenger, Krausmann, and Schandl (2010) analysed the role of useful exergy along the technical energy chain for the UK, US, Japan and Austria.Foxon (2015, Brockway, Barrett, Foxon, & Steinberger, 2014 updated this analysis for the US, UK and China whilst Serrenho et al. (2014) did the same for the EU-15. Regarding developing economies, Heun and Brockway (2019) calculated the long-term resource intensity at the useful stage for Ghana, Jadhao, Pandit, and Bakshi (2017) did the same for India and Guevara, Sousa, and Do ...
Article can be downloaded via share link at: Energy and materials support food production, maintain and expand material stocks (e.g. buildings and roads) and provide services. In this paper, an exergy-based approach is used to provide an integrated perspective on the evolution of societal resource flows and stocks. The scope of this analysis is from resource extraction (primary exergy stage) to end uses such as low temperature heating and illumination (useful exergy stage). From 1900 to 2010, global exergy consumption at the primary stage increased from 115 to 903 EJ/year, of which 88-89% corresponded to energy flows, including food and feed. Useful exergy flows increased from 9 to 148 EJ/year, of which 47%, in 2010, was contained within material goods. Primary to useful efficiency doubled from 8% in 1900 to 16% in 2010. However, this improvement is far from that which is required to achieve climate targets for 2060. The amount of resource flows required per unit of economic activity decreased at both the primary (from 58.5 to 17.0 GJ/$) and useful (from 4.7 to 2.8 GJ/$) exergy stages, indicating relative decoupling. The exergy in stocks went from 91 to 820 EJ. Stock intensity reduced from 46.2 to 15.5 GJ/$-year − 1 due to a shift in stock composition rather than dematerialisation in mass terms. Future research needs to identify the relationships between resource flow intensity and stock intensity in order to meet sustainability targets, including those linked to future resource demand. The scope could be expanded to include additional resources such as water and rare earth metals.
... Further examples regarding other countries or specific sectors can be found in the literature (e.g. [13][14][15][16][17][18]). Figure 3 shows that Norwegian resources come from waterfalls, oil and gas, mineral ores, forests, and agricultural products. These are being used to produce useful products and services such as paper, metals, chemicals, lighting, transport and space heating. ...
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In a world where resources are limited and their use will necessary have an impact, efficient handling of resources becomes essential. This review concerns three thermodynamic tools that can be systematically used to evaluate and improve resource use. The tools are related to the second law of thermodynamics, which sets a general framework for all conversion processes, including food processing. We address the benefits of using exergy analysis to map the losses of energy quality in a process. This can be done at every scale, from a nation scale to a process unit, and the results of the analysis can be used at different levels of decision making processes, by policy makers, plant managers, scientists or engineers. Moreover, knowledge on coupling between transport processes can be used to drive processes using driving forces other than the conventional ones. This would allow us to recover some of the resource potential which is currently wasted (e.g. process waste heat). Finally, inspiration for efficient design can be found in nature; two examples of nature-inspired chemical engineering (NICE) design are reviewed and used to encourage a development in this direction.
... Thus, we argue that given these rigidities, some industries need to adapt in advance, even more that the others, to an eventual lack of energy source across all the supplychain, i.e. not only the abovementioned sectors have to reduce their dependency on NRER -if possible at all-but industries depending on these industries like tourism, construction or all manufacturing industries relying on steel. By all means, adaptation in all sectors would require an absolute reduction in energy use that could be faced by energy efficiency at the device level, but especially by demand-side management policies, given the proximity to the thermodynamic limits to the former in advanced economies (Groscurth, Kümmel and Van Gool, 1989;Ayres, 2007;Brockway et al., 2015) and the potential effectivity of the latter (Creutzig et al., 2016(Creutzig et al., , 2018. However, on top of that, industrial policy has revealed as an utterly relevant tool of sectoral adaptation to eventual energy scarcity, despite it has been ruled out in practice over the last few decades. ...
Rapid expansion, relative shortage resources supply and environmental impact threat the sustainable development of the smelting and pressing of metals sector. Fluxes of energy, materials, environmental remediation expenses, labor, and capital were quantified by Joules based on the second-law thermodynamics during years 1992–2015. The accounting method that quantifies the component of the extended exergy fluxes and the proportion in the total inputs was used to analyze this energy-intensive industry. Net per-capita exergy resource input and labor production efficiency are described the conversion of natural resource exergy to economic output and labor efficiency. The results showed the following: (1) the smelting and pressing of metals sector expands rapidly; the ferrous metals industry accounts the large part of the overall metals industry and the nonferrous metals industry grows faster than the ferrous metals industry. Natural resource exergy, especially energy exergy, dominates the investments of the metals industry. (2) Capital exergy and labor exergy decrease in the smelting and pressing of metals industry, while they increase in the nonferrous metals industry and decrease in the ferrous metals industry. Environmental exergy declines in both the nonferrous metals and ferrous metals industries. (3) The comparison of the nonferrous metals and ferrous metals industries with China as a whole, conducted by applying the two indicators for efficiency, shows that the two industries are exceeding the whole country in efficiency and have made great progress. In addition, the extended exergy analysis of smelting and pressing of metals industry is helpful in the identification of resource consumption and environmental cost in sustainable development view.
With the rapid popularization of China’s agricultural mechanization, energy consumption in China’s agricultural activities has been increasing significantly. Based on the agricultural sample data from 2000 to 2017, this study explores the substitution effect and rebound effect of China’s energy consumption in agricultural sector. In order to characterize the asymmetric response, this paper applies asymmetric price decomposition method and seemingly unrelated regression model to conduct the research. Some important results are found as follows. First, own-price elasticity of energy, labor and capital in China’s agricultural sector are −0.25, −0.23, and −0.33 during the study period, showing all the inputs are lacking of price elasticity of demand. Second, the substitution rate of capital for energy is 0.21 while the substitution rate of energy for capital is 0.17, which indicates that reducing capital cost is a more effective way than raising energy price for agricultural energy-saving goal. Second, the rebound rate of energy consumption is 74.78%, meaning that the improvement of energy efficiency relying on technological progress can only achieve 25.22% of energy saving effect. Fourth, the energy rebound rate of China’s agricultural sector is much higher than those of other sectors and presents certain regional characteristics over the study period. Based on these research results, measures such as accelerating market-oriented reform of input factors, increasing agricultural subsidies, optimizing agircultrual energy structure, and weeding out backward agricultural machineries are put forward for the development of China’ agricultural sector.
Urban energy metabolism reflects the energy transmission and exchange process to uncover the structure and functionality of urban activities. The literature on urban energy metabolism is overwhelming and still growing. However, few publications have attempted to provide a specific literature review of urban energy metabolism. Therefore, this study conducted a systematic bibliometric analysis on urban energy metabolism publications to explore research status and emerging trends. Results showed that the amount of co-cited literature on urban metabolism displayed an upward trend during the study period. After 2006, the number of urban metabolism publications implied an upsurge. Based on the co-word analysis of keywords, “energy” occupied much of the attention of urban metabolism research. The high citation keywords relating to network analytical methods in urban energy metabolism publications were analyzed via the co-word network, including ecological network analysis, input-output analysis and the complex network. The combination of input-output analysis and ecological network analysis has been widely applied to urban energy metabolism studies worldwide, especially in the context of China. Consequently, future research opportunities were suggested from the following aspects: (1) the inclusion of both spatial and temporal dimensions of energy metabolic systems, and (2) the ability to unravel the interactions of components in a dynamic manner. The findings of this study were not only beneficial for scholars to detail a holistic picture of current research progress, remained questions and emerging research methods, but also valuable to assist practitioners to evaluate and monitor the urban energy metabolic performance with suitable network methods.
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This thesis is directed towards the issue of the long-term demand and supply of biomass for food, energy and materials. In the coming decades, the global requirements for biomass for such services are likely to increase substantially. Therefore, improved knowledge of options for mitigating the long-term production requirements and the associated effects on the Earth system is essential. The thesis gives a thorough survey of the current flows of biomass in the food system. This survey was carried out by means of a physical model which was developed as part of the work. For eight world regions, the model is used to calculate the necessary production of crops and other phytomass from a prescribed end-use of food, efficiency in food production and processing, as well as use of by-products and residues. The model includes all major categories of phytomass used in the food system, depicts all flows and processes on a mass and energy balance basis, and contains detailed descriptions of the production and use of all major by-products and residues generated within the system. The global appropriation of terrestrial phytomass production induced by the food system was estimated to some 13 Pg dry matter per year in 1992-94. Of this phytomass, about 0.97 Pg, or 7.5 percent, ended up as food commodities eaten. Animal food systems accounted for roughly two-thirds of the total appropriation of phytomass, whereas their contribution to the human diet was about one-tenth. Use of by-products and residues as feed, and for other purposes within the food system, was estimated to about 1.8 Pg dry matter, or 14 percent of the total phytomass appropriation. The results also show large differences in efficiency for animal food systems, between regions as well as between separate commodities. The feed conversion efficiencies of cattle meat systems were estimated to about 2 percent in industrial regions, and around 0.5 percent in most non-industrial regions (on gross energy basis). For pig and poultry systems, feed conversion efficiencies were roughly a factor of ten higher. The differences suggest that there is a substantial scope for mitigating the long-term production demand for crops and other phytomass by increases in efficiency and changes in dietary preferences.
In this paper, the global visual servoing micropositioning control method is studied and the imaging model is derived based on the G type stereo light microscope (SLM). The model contains no depth information and the left and right image information is used to obtain the image Jacobian matrix. Considering the dynamic characteristics of microrobot, we design an image-based controller, and then the stereo microscopic visual servoing system is transformed into a Hamiltonian system. It will simplify the stability analysis. The experimental and simulation results based on the four degree-of-freedom (DOF) microrobot system demonstrate the validity of the theory in this paper.
Aims: The industrial sector dominates the China's total energy consumption, accounting for about 70% of energy use in 2010. Hence, this study aims to investigate the development path of China's industrial sector which will greatly affect future energy demand and dynamics of not only China, but the entire world. Scope: This study analyzes energy use and the economic structure of the Chinese manufacturing sector. The retrospective (1995-2010) and prospective (2010-2020) decomposition analyses are conducted for manufacturing sectors in order to show how different factors (production growth, structural change, and energy intensity change) influenced industrial energy use trends in China over the last 15 years and how they will do so up to 2020. Conclusions: The forward looking (prospective) decomposition analyses are conducted for three different scenarios. The scenario analysis indicates that if China wants to realize structural change in the manufacturing sector by shifting from energy-intensive and polluting industries to less energy-intensive industries, the value added average annual growth rates (AAGRs) to 2015 and 2020 should be more in line with those shown in scenario 3. The assumed value added AAGRs for scenario 3 are relatively realistic and are informed by possible growth that is foreseen for each subsector. Published by Elsevier Ltd.
During this study a methodology was developed to project growth trends of the motor vehicle population and associated oil demand and carbon dioxide (CO2) emissions in China through 2050. In particular, the numbers of highway vehicles, motorcycles, and rural vehicles were projected under three scenarios of vehicle growth by following different patterns of motor vehicle growth in Europe and Asia. Projections showed that by 2030 China could have more highway vehicles than the United States has today. Three scenarios of vehicle fuel economy were also developed on the basis of current and future policy efforts to reduce vehicle fuel consumption in China and in developed countries. With the vehicle population projections and potential vehicle fuel economy data, it was projected that in 2050 China's on-road vehicles could consume approximately 614 million to 1,016 million metric tons of oil (or 12.4 million to 20.6 million barrels per day) and emit 1.9 billion to 3.2 billion metric tons (or 2.1 billion to 3.5 billion tons) of CO2 each year. Although these projections by no means imply what will happen in the Chinese transportation sector by 2050, they do demonstrate that an uncontained growth in motor vehicles and only incremental efforts to improve fuel economy will certainly result in severe consequences for oil use and CO2 emissions in China.
In order to better understand sectoral greenhouse gas (GHG) emissions in China, this study utilized a logarithmic mean Divisia index (LMDI) decomposition analysis to study emission changes from a sectoral perspective. Based on the decomposition results, recently implemented policies and measures for emissions mitigation in China were evaluated. The results show that for the economic sectors, economic growth was the dominant factor in increasing emissions from 1996 to 2011, whereas the decline in energy intensity was primarily responsible for the emission decrease. As a result of the expansion of industrial development, economic structure change also contributed to growth in emissions. For the residential sector, increased emissions were primarily driven by an increase in per-capita energy use, which is partially confirmed by population migration. For all sectors, the shift in energy mix and variation in emission coefficient only contributed marginally to the emissions changes. The decomposition results imply that energy efficiency policy in China has been successful during the past decade, i.e., Top 1000 Priorities, Ten-Key Projects programs, the establishment of fuel consumption limits and vehicle emission standards, and encouragement of efficient appliances. Moreover, the results also indicate that readjusting economic structure and promoting clean and renewable energy is urgently required in order to further mitigate emissions in China.