The 34th International Conference of the System Dynamics Society, Delft, Netherlands, July 17 — 21, 2016.
In Sum, Nations Will Likely Fulfill Their Pledges to Reduce
Greenhouse Gas Emissions: A Stock-and-Flow-Based View
Bent Erik Bakken, Onur Ozgin
DNV GL Strategic Research & Innovation
Veritasveien 1, 1363 Hovik, Norway
bent.erik.bakken@ dnvgl.com, onur.ozgun@ dnvgl.com
Abstract
Emission reduction pledges of almost all UN member states, intended nationally
determined contributions (INDCs), were signed in December 2015. If they are met,
world temperature will stabilize at about 3°C warmer than pre-industrial levels. But
will they be met? Commonly, business-as-usual scenarios predict 5-6°C warming and
limiting warming to 3°C is regarded to be an arduous task. We however find that
achieving the 3°C warming indeed results from business-as-usual past trends in energy
investments. Using a stock-and-flow framework for estimating average energy capacity
additions for five regions of the world, we conclude that extrapolation past investment
trends also gives a 3°C warming, implying that, in sum, INDCs will likely be met. But
limiting warming to 3°C is clearly not enough: We use the same stock-and-flow model
to show that a combination of increased energy efficiency, facing out all fossil
investments before 2030 and implementation of carbon capture and storage is required
to limit global warming to 2°C.
Background
Limiting climate change will be key to future human wellbeing. The COP21 agreement
achieved in Paris in 2015 (Paris Agreement, 2015) and about to be signed by most UN
member states in 2016 is good. On the one hand, nations pledge to reduce their
emissions, but on the other hand pledges implied by the INDCs (intended nationally
determined contributions), covering mainly the 2015-2030 period, will only ensure that
the global temperature rise is limited to 2.7°C (CI, 2015) or 3.5°C (CAT, 2015) above
pre-industrial temperatures, depending on post-2030 assumptions. On the other hand,
the final COP21 document reflects that the pledges are far from sufficient, and in order
for global warming to stop before 2°C, parties must later meet to further tighten their
INDCs.
Analyses of likely future temperature rises, assuming no new policies, are yet far
bleaker. Current energy and related greenhouse gas (GHG) inertias imply far higher
emissions than those implied by nation’s pledges: Emissions are consistent with a
5-6°C (IPCC, 2014; IEA, 2015) future. If so, limiting warming to 2.7-3.5°C will be
extremely challenging. Achieving, as the COP21 final agreement text states, a world
where emissions are cut in line with a 2°C warming will be almost impossible.
The 34th International Conference of the System Dynamics Society, Delft, Netherlands, July 17 — 21, 2016.
Yet other voices (Stern et al, 2016) note that some countries like China, which is
currently responsible for about 1/3 of global GHG emissions, may already have seen its
GHG emission peak. Stern et al. claim that China will not only meet, but significantly
overachieve its INDC pledge of peaking GHG emissions before 2030. Their arguments
hinge mostly on two strong trends that they find already well established, and that the
newly agreed upon Chinese five-year plan further bolstered: The Chinese are embarking
on a transformation from an industrial to a service economy. Its GDP growth will thus
be tempered. Additionally, the authors find a strong transition from fossil to renewable
energy sources.
This paper probes further into this inertia question, and addresses whether current
policies in place will lead us to a 3°C world. The difference has major implications on
both the likelihood of achieving the INDCs and thus how draconian measures will have
to be to meet the demands of a lively future planet Earth.
We first present other business-as-usual scenarios and compare them with our
understanding of business-as-usual. Then, by comparing our results instead with INDC
scenarios, we establish that our business-as-usual scenario is actually in agreement with
the path suggested by INDCs. At the end, we present a modified version of our
approach to demonstrate what is necessary to limit global warming to 2°C.
Business as usual
The term “business as usual” may be interpreted in a number of different ways. In the
context of climate change, the interpretation may vary from assuming no change in
energy mix, no change in the current energy intensity of the economy and/or continued
economic growth at current levels, to a softer interpretation that there will not be major
changes in policies while allowing current trends in technology, society and economy to
continue. In this section, we interpret two widely accepted business-as-usual scenarios.
IPCC
For decades, the Intergovernmental Panel on Climate Change (IPCC) has issued reports
on various pathways that the world might follow in terms of GHG emissions and their
temperature consequences (IPCC, 2014). IPCC uses one classification as Business As
Usual (BAU). Though clearly not defined as a likely future emissions pathway, its
Representative Climate Pathway (RCP) 8.5 is a reference, attempting to capture future
emissions if inertias reflected in current GHG related policies continue. RCP 8.5 is
described in Riahi et al. (2011), and uses IIASA Integrated Assessment Modeling
Framework. RCP 8.5 is characterized by rapid population growth accompanied by a
slow per-capita income growth, limited technology growth, and hence slow
improvement in energy intensity. High energy demand, accompanied by a fragmented
geopolitical scene, slow economic and technological growth creates an energy supply
highly dependent on fossil fuels (Riahi et al. 2011).
The 34th International Conference of the System Dynamics Society, Delft, Netherlands, July 17 — 21, 2016.
This pathway implies that GHG emissions will more than double from current levels of
about 50 GtCO2/year to about 100 GtCO2/year as they flatten out 100 years from now.
How the climate will handle such CO atmospheric concentrations is uncertain but the
global temperature rise will continue to rise from a level of about 5°C mid next century.
For simplicity, we call this a 6°C world.
Table 1: The bottom RCP 8.5 is the IPCC BAU, where temperature will continue to increase
even after 2100 and probably not stop before 6°C (IPCC, 2014).
Change in COze
COz-eq iGiative pera emissions compared to| ‘Temperature change (relative to 1850-1900)
‘i 2010 (%]
22100 [pm ies Posticn of 2100 | Likelihood of staying below temperature
CO2eq) 2011-2050|2011-2050] 2050 | 2100 |r level over the 21° century
; change[*C][ 15% | 20°C | 30°C | 40°C
. 41-48 Mor
>1000 ffotal range RCP8.5 |1840-2210|5350-7010| 52t095 | 74t0178| 3's 75) Unlikely | Unlikely | unlikely
; than likely
Figure 1 below shows corresponding GHG emissions form the RCP 8.5. The BAU
increase is from 50 GtCO2-eq in 2013 to 65 by 2030, or 30 %.
140
[Hl >1000 ppm CO,-eq — 80th Percentile
120 — Median RCP8.5
— 10th Percentile
Annual GHG emissions (GtCO,-eq/yr)
0
2000 2020 2040 2060 2080 2100
Year
Figure 1: GHG emissions from the RCP 8.5 (IPCC, 2014)
The International Energy Agency (IEA) annually issues its World Energy Outlook (IEA,
2015), where three future scenarios are covered in some detail. CPS (Current Policies
Scenario) is the IEA’s business-as-usual scenario. The Current Policies Scenario
purports to “take into consideration only those policies for which implementing
measures had been formally adopted and make the assumption that these policies persist
unchanged”.
By 2030, emission increase to 39.1 GtCO, from 31.6 in 2013 (energy system related), or
24% (our linear interpolation between 2020 and 2040, to achieve 2030 figures, as 2030
The 34th International Conference of the System Dynamics Society, Delft, Netherlands, July 17 — 21, 2016.
is not mentioned in their analysis). The scenario is not defined beyond 2040, but there is
no indication of GHG emissions plateauing. Though the GHG growth rate is lower than
IPCC’s, our assumption is that the CPS will also plateau in about 100 years at a level
slightly below RCP 8.5, i.e. with a temperature of about 5°C above the 1850-1900
average.
Ours
We interpret business as usual in line with Forrester (1961), that the underlying decision
making is not changed. Forrester noted that a system’s flows capture its decision
making. If the information sources remain unchanged, and use of these sources (as
captured by the equations using the information sources, frequently referred to as
decision rules) do not change, System Dynamics tradition would define decision rules
as business as usual: business as usual implies constancy of decision rules.
Our interest is in the future behavior of world GHG emissions, notably the most
important part of this, the fossil energy’s. We conceptualize this as the stocks of various
energy sources capacity to bum fossil fuels. An energy capacity buming stock is
depleted mainly by capital decay, and replenished by (gross) capacity additions. A
reasonable good source of most nations’ energy consumption is BP’s database
(BP, 2015), complemented by IEA estimates capital life times for various energy assets.
Thus our method is captured in Figure 2 below: As we know the historical energy
consumption pattern, and capital lifetimes, we can synthesize what the capacity addition
must have been. Details of the calculations are presented in A ppendix 1.
Synthetic Historical Estimated
Capacity Energy Capital
Additions Consumption Decay Capital Lifetime
a ea
Figure 2: Synthesizing the flow: capacity additions
We divide the world into five representative regions of various economic development
classes, in line with the classification used by Randers (2011) (USA, China, rest of
OECD, emerging economies: BRISE, rest of the world) and estimate capacity additions
flows over the last 13 years in the eight major energy carriers (coal, oil, gas, nuclear,
hydro, solar, wind and biomass). The synthetic capacity additions are then analyzed to
establish whether there was any continuity. We first note that the BP data on
consumption, in fact, mixes two aspects of energy use,
1. The capacity to use energy,
The 34th International Conference of the System Dynamics Society, Delft, Netherlands, July 17 — 21, 2016.
2. Utilization of this capacity.
Our interest is mostly in the former. Therefore, we assume that capacity utilization will
be constant over time (a reasonable assumption over long time horizon, considering e.g.
for hydropower, one year of little water in the hydropower dams is followed by more
water and thus hydro energy consumption the next). Smoothing by using a running
average of our annual synthetic capacity additions is thus performed. Based on
historical smoothed flow pattems, we distinguish three different trend types of capacity
additions: Linear-, quadratic- and exponential growth. We test all three trend fits to the
flow data series in question and choose whatever trend fits the data best, with some
exceptions where we added our judgment—see A ppendix 1. An example is presented in
Figure 3. Types and parameter of trend fits for all regions and energy carriers are
presented in A ppendix 1.
f=}
84
50
grein, jf cxmal
oe 4
T T T T —
1980 2000 2020 2040
Figure 3. An example trend analysis for coal capacity additions for BRISE countries. In this
case best fit was quadratic trend.
Figure 4 shows how we first smooth the Synthetic Capacity Additions, and next how we
establish Historic Capacity Additions Trends and apply these into the future as Trend
Based Capacity Additions. Detailed description of the method is also relegated to
Appendix 1.
Smoothed Synthetic Historic Capacity
Synthetic Capatity Additions
Capacity Additions Additions Trends
O
Trend Based
SSCA Capacity Additions
Change
Smoothing Trend Time
Time Constant Constant
The 34th International Conference of the System Dynamics Society, Delft, Netherlands, July 17 — 21, 2016.
Figure 4: Trend Based Capacity Additions captures the inertia of past decision making.
The formulation captured in Figure 4 deviates somewhat from the common System
Dynamics formulations inasmuch as the decision maker is absent; there is no
representation of someone who operates on basis of stocks and flows surrounding
him/her. We still think our approach captures another typical feature of System
Dynamics, namely, decision inertia: Instead of establishing how the myriad of public
and private investors react to multitude of signals—such as expectation of future profit
levels for various investments or emissions reduction ambitions—we simply integrate
all such factors into the experienced inertia of investment behavior, i.e. capacity
additions.
Will the future be like the past?
The analysis further below assumes that business as usual implies that the past is a good
predictor of the future. But will the history repeat itself in terms of energy trends? If this
historical period can be regarded as an anomaly, then business during the period in
question was not usual.
However, during the 2000-2013 time period, which forms the basis for our trends, the
world saw a representative period of economic ups and downs. It included the 2000-
2007 strong global GDP growth and high energy prices, the financial crisis 2007-2010
and extremely low energy prices, again followed by higher prices at the end of the
period towards 2013. The entire period was one with significant renewable energy
subsidies, but also of even more fossil subsidies. We believe that the next 15 years both
renewables and fossils subsidies will change, but neither will disappear, and globally
they will have a similar effect in the next 15 years as they had in the previous 15.
Applying past flow trends to the future
We extrapolate the trends established above from 2015 through 2030, thus establishing
energy supplies for five regions and eight energy carriers. However, these trends are not
directly used to determine energy use by source. In order to make sure that the sum of
these trends is in line with future energy demand, we first forecast regional energy
demand using determinants of energy demand, and then adjust the trends so that sum of
energy capacities is equal to the energy demand. To establish total energy demand, we
use the first three terms of the Kaya identity (Kaya and Keiichi, 1997),
F=Pxxixt (1)
where F is CO2 emissions from human sources, P is population, G is GDP, E is energy
demand. The terms G/P, E/G, F/E correspond to GDP per capita (productivity), energy
intensity of economy, and emission intensity of energy use, respectively. For each of the
The 34th International Conference of the System Dynamics Society, Delft, Netherlands, July 17 — 21, 2016.
five regions, we decompose total energy demand in three components (population,
productivity and energy intensity) and separately estimate each component.
E = Eust+ Echina + Eozcp + Eprise + Erow (2)
E,=P,x 2x (3)
PG
where r denotes the region.
Each of the components of Equation 3 is forecast by establishing first a history of the
change rate of the corresponding variable at the regional scale, and establishing a trend.
The details of these forecasts are beyond the scope of this paper. The final regional
forecasts for population, productivity and energy intensity are presented in A ppendix 2.
We use the following procedure to calculate the energy supply and CO2 emissions by
eight energy carriers:
1. Establish total energy demand, using Equation 3
2. Run the System Dynamics model presented in Figure 4 separately for each of
the five regions, using the following built-in decision rules,
a. Establish future energy supply based on the capacity additions trends,
separately for each of the eight energy carriers
b. In case of regional oversupply: Force retire oil, coal and gas capacities to
establish balance
c. In case of regional shortfall: Add renewable capacity in trend’s
proportions (This decision rule was never applied, as shortfall never
occurred in any region)
3. Using Equation 1, calculate CO, emissions using Energy Supply and emission
intensities of energy sources, available from literature.
The 34th International Conference of the System Dynamics Society, Delft, Netherlands, July 17 — 21, 2016.
02
Trend Based
Capacity Additions Emissions Capitaf Life Time
Energy Supply
O 63
Capital Decay
ne)
Energy Demand
Energy Gap Forced Retirements
Figure 5: Establishing energy surplus and shortfalls.
Figure 5 reflects an anchoring and adjustment decision rule. The anchor is the inertia of
past trends, the adjustment occurs when these trends produce energy shortfalls or
surpluses. If so, surplus is removed through shutting down of the coal power plants (at
that time, they will also be the least profitable), shortfalls will be handled by adding
non-fossil capacity, as these are also the least costly.
What will be the GHG emissions c ses of busi: as usual?
Figure 6 presents the resulting CO2 emissions for the “Trends” scenario that includes
energy-related emissions as well as constant 6 GtCO./yr to account for cement
production, land use change and forestry emissions.
40
35 preset se
30 “s) ____usAa
5
Sas —oecD
g 20 —— CHINA
Oo 45 ——BRISE
10 —ROW
— World
5
0: ;
1970 1990 2010 2030 2050
Figure 6: CO, emissions under “Trends” scenario.
In Table 2, we compare our business-as-usual projections with others’.
The 34th International Conference of the System Dynamics Society, Delft, Netherlands, July 17 — 21, 2016.
Table 2: Our business-as-usual scenario, “Trends”, against others’.
Scenario Emission types 2015 — 2030 Remarks
covered global growth
Energy (+ cement, land use
“Trends” CO» emissions 5% change and forestry
issions at current level)
TEA Current Policies | CO. energy related 4%
Scenario (CPS) emissions
IPCC RCP 8.5 All GHG emissions 30%
As seen in figure 6, “Trends” emissions will peak within the next 15 years. The IPCC
RCP 8.5 is defined for another hundred years, and emissions will not peak within that
time horizon. IEA CPS is only defined through 2040, but at that point still shows strong
emissions growth.
What will be the global consequences of INDCs?
Business as usual will deliver global INDCs
We have shown that “Trends” result in far less emissions than the other business-as-
usual scenarios. We now investigate whether it creates emissions that align with the
INDCs. One major problem with the INDCs is the difficulty to translate the descriptions
into consistent and reliable emission pathways. This is either because nations did not
make clear what methodology they use in calculating their base emissions and targets,
or because some pledges are only qualitative in nature. To address this problem, we use
three independent analyses of the effects of INDCs on GHG emissions, done by Climate
Interactive (2015), Climate Action Tracker (2015), and IEA (2015) in their most recent
“New Policies Scenario” (NPS). They differ, in as much as IEA only looks at energy-
related CO2 emissions, Climate Action Tracker only looks at CO2 emissions (including
also non-energy ones) and Climate Interactive looks at all GHG emissions, converted
into CO2 equivalents. Currently, energy CO2 emissions are about % of all CO
emissions. CO constitutes about 34 of total GHG emissions. Our “Trends” business-as-
usual scenario (similar to Climate Action Tracker), is compared these INDC scenarios
in Table 3 showing total emissions growth 2015-2030.
Table 3: GHG emissions growth 2015-2030
Emission types 2015 — 2030
Reference covered global growth Remarks
« ” 5 oe Energy + (current level
‘Trends’ CO, emissions 5% cement + LUCF)
IEA CO, energy 9%
New Policies Scenario |_related emissi g
Climate Interactive All GHG 0%
INDC emissions °
Climate Action Hae Energy + Cement +
Tracker INDC All CO2 emissions 9% LUCE + other
The 34th International Conference of the System Dynamics Society, Delft, Netherlands, July 17 — 21, 2016.
Table 3 illustrates that “Trends”, though being presented as a business-as-usual scenario,
produces global emission growth in line with the three INDC studies.
But what about regional emissions? Table 4 compares the two INDC interpretations for
China and USA, where INDC projections are available. Other than the fact that regions
below exactly replicate each other’s geography, the two regions in question also cover
almost half of global GHG emissions in 2015: emissions from USA and China
contribute to 15% and respectively 30% of global CO2 emissions.
Table 4: GHG emissions growth 2015-2030
World USA China
“Trends” 5% —32% 3%
IEA New Policies Scenario 9% =11.% 11%
Climate Interactive INDC O%| -28% 22%
In Table 4, one can note that while “Trends” allows for substantial increases in GHG
emissions outside the emissions-declining USA and China, IEA and Climate Interactive
forecasts strong emissions growth in China, which implies that very little emissions
growth can take place elsewhere. The conclusion is that although the emission pathway
of “Trends” is very close to the emission pathway of INDCs at the global level, they
agree less at the regional level.
In Table 5, we further compare regional differences in fossil energy. Climate Interactive
did not provide such data (but as seen in Table 4, Climate Interactive and IEA INDC
analyses are comparable.) IEA does provide energy source breakdown.
Table 5: Growth in energy use 2015-2030
Total
Energy Oil Gas Coal
World “Trends” 14% -4% 15% 1%
TEA NPS 18% 8% 10% 7%
USA “Trends” —20 % 65 % 14% —46 %
TEA NPS 2% “HN% 7% 36 %
China “Trends” 22% al% 159 % -12%
TEA NPS 22% 26% 126 % 0%
In line with Table 4, “Trends” not only expects reduction in US’ energy consumption by
20%, but also foresees a cut in coal consumption by half and oil consumption by almost
2/3—and gas consumption to increase by 14%. The IEA New Policies Scenario (NPS)
sees the total world consuming ever more oil and coal, while consuming far less gas.
But in NPS these trends do not hold for the US, where coal and oil use is curbed
significantly and, like in China, gas use is increased.
The 34th International Conference of the System Dynamics Society, Delft, Netherlands, July 17 — 21, 2016.
What will it take to limit the global warming to 2°C?
Contrary to benchmark business-as-usual calculations of IEA and IPCC, our study
indicates that INDC pledges reflects business-as-usual for the world as a whole, but
with regional differences: Especially China will likely overachieve its INDC pledges, in
line with Stem et al (2016). By the same token, USA will meet its pledges following
well-established investment inertias in fossil and non-fossil fuels. This means that the
rest of the world cannot follow business as usual, but must work hard to fulfill its
pledges.
As is clear from the Paris Agreement (2015), however, INDC pledges will not be
sufficient in limiting global warming to sustainable levels. We (among others, see e.g.
IEA, 2015 and IPCC, 2015) have also investigated a plausible scenario limiting global
warming to 2°C. We have compared the “Trends” energy system (that produces a 3°C
world when applied throughout 2100) to one that one in line with a 2°C world. The
measures will have to be extremely draconian. Compared to business as usual as
established above, we have (Bakken & Ozgiin, 2016)
- Starting in 2015, reduced all fossil capacity additions linearly to 0 by 2030, and
replacing the needed energy with non-fossil sources, in proportion to what
“Trends” used,
- Significantly decoupled energy use from economic growth by increasing regions’
annual energy efficiency improvements by 75% from what they were in “Trends”
(thus reducing GDP’s global energy intensity to 1/3 of today’s levels, compared
to “Trends” reduction to 4),
- Beginning in 2030, linearly increased use of carbon capture and storage so that it
covers 50% of all gas and coal usage in 2050,
Conclusion
Commonly, COP21 is seen as a major change in nations’ commitment to solving the
galloping global CO2 emissions that will lead to serious climate change. We instead find
that INDCs can be explained as extensions of policies already in place. As China
“automatically” will overachieve on its pledges, and USA will deliver on them, it is
only the rest of the world that will have to struggle and improve on its current policies.
In a sense, nations and businesses have—seen as a whole—built up momentum that will
lead them to fulfill the sum of pledges.
Meeting INDCs will not come automatically, but only require that existing level of
decarbonization decision making continue. Hence, there is additional room for nations
to up the stakes and provide more draconian measures. This is really needed, as the
INDCs do not deliver a sustainable planet, but are consistent with a steadily warmer and
climatically highly problematic future. Meeting INDCs will lead to stabilization around
The 34th International Conference of the System Dynamics Society, Delft, Netherlands, July 17 — 21, 2016.
3°C, far from sufficient for reaching the COP21 objectives of limiting global warning to
2°C;
Focusing on the energy system, which is the main cause of global warming, the present
study points to that strategies that will lead us to a 2°C are also feasible. This will mean
increasing by %4 the rate of energy efficiency improvements, and a similar increase in
the rate of uptake of renewable sources. This will also imply that no investments can be
done to develop and extract from new fossil fields after 2030. That will be a challenge,
in contrast to meeting the INDCs, and require fundamental transformation of the
regional energy and other systems that emit GHG. We have no time to lose.
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The 34th International Conference of the System Dynamics Society, Delft, Netherlands, July 17 — 21, 2016.
Appendix 1
Below we summarize the details of the processes described in the main text. Variable
names are in boldface.
1. We used BP energy consumption data (BP, 2015) (1965 to 2014) as Historic Energy
Consumption (Figure 2). We assumed 2015 consumptions to be equal to those of 2014.
2. To calculate regional totals, we assumed Russia's and Ukraine's 1985 energy
consumption shares in Soviet Union's are constant from 1965 to 1985.
3. We estimated Capital Decay assuming that Historic Energy C onsumption to be
removed at any year is equal to its Historic Energy Consumption divided by its
Capital Lifetime (first-order delay), assuming following lifetimes: coal—50 yr,
oil—50 yr, gas—50 yr, nuclear—50 yr, hydro—70 yr, solar—s0 yr, wind—50 yr,
other renewables—40 yr
4. We estimated Synthetic Capacity Additions by taking the difference between two
consecutive years' Historic Energy C onsumptions and adding Estimated C apital
Decay for that year, allowing negative Synthetic Capacity Additions.
5. For years 1970 to 2010, we calculated Smoothed Synthetic Capacity Additions
(Figure 4) by taking mean values of Synthetic Capacity Additions from 4 years earlier
to 4 years ahead of any year (covering 9 years)
6. For non-renewables from 2011 to 2014, we calculated Smoothed Synthetic
Capacity Additions by taking mean values of Synthetic Capacity Additions over
shorter intervals, covering symmetrical number of years from current year to 2014 and
from current year back to same number of years.
7. For renewables, we calculated Smoothed Synthetic Capacity Additions of 2011 by
taking mean values of Synthetic Capacity Additions from 3 years earlier to 4 years
ahead.
8. For renewables from 2012 to 2014, we calculated Smoothed Synthetic C apacity
Additions by multiplying Estimated Total Renewable A dditions with Estimated
Share of Renewables in Additions. Estimated T otal Renewable Additions for each
The 34th International Conference of the System Dynamics Society, Delft, Netherlands, July 17 — 21, 2016.
region is calculated from linear trend extrapolation model between the sums of 2005-
2011 renewable Smoothed A dditions and T otal Investments in Renewables. Total
Investment in Renewables shows total monetary investments made in renewables and
is taken from UNEP Bloomberg New Energy Finance report (McCrone et al 2015). For
USA and China, the data was available. For OECD, the data is estimated by subtracting
USA's investments from Developed countries' investment. BRISE data was estimated
by adding investments of Brazil and India (for both, data is available for all years) and
South A frica, Mexico, Turkey, Chile, Thailand, Vietnam, Iran, Argentina and
Venezuela (for which data availability varied from year to year). Rest of the world
(ROW) data was estimated by subtracting USA, China, OECD and BRISE estimates
from the World total. Estimated Share of Renewables in A dditions represent the
shares of Hydro, Solar, Wind and Other Renewables in total investment in renewables,
and are estimated by extrapolating the global trends of capacity per unit investment
ratios of 2004-2011 period (with different periods and curve types used for different
energy sources) to 2012-2014, and multiplying this with regional investment estimates
(which is calculated by applying 2012-2014 global growth trends of renewable energy
sources to 2011 estimates of regional energy investments).
9. Trend Based Capacity Additions use one of three time-series model to forecast
capacity additions for years 2015 to 2050 based on Smoothed Synthetic Capacity
Additions series from 2004 to 2014. These time-series model types use one of two data
sources: Synthetic Capacity Additions or Changes to Synthetic Capacity Additions:
for both, linear extrapolations were attempted. For Synthetic Capacity Additions,
quadratic polynomial was also attempted. The rule was to use linear extrapolations to
Synthetic Capacity Additions, with the following exceptions: quadratic polynomial
extrapolations based on Synthetic Capacity Additions for USA Gas (since linear
extrapolation showed unrealistic growth—tripling from 2015—quadratic polynomial
was more realistic as it was the faster declining option), China Solar (since the other
two alternatives were unable to reflect the expected accelerating growth), ROW Coal
(since the other two alternatives showed decline, while rising energy demand in ROW
dictates capacity additions); linear extrapolations based on C hanges to Synthetic
Capacity Additions for China Oil (since linear extrapolation showed unrealistic growth
and the selected option dictated the least capacity additions—in line with expectations),
15
The 34th International Conference of the System Dynamics Society, Delft, Netherlands, July 17 — 21, 2016.
OECD Gas (since other two models indicated immediate decline to zero at year 2015),
USA Other Renewables, China Other Renewables, OECD Other Renewables, BRISE
Other Renewables (since this option showed boom-and-bust behavior for all four ‘Other
Renewables’ series—the most reasonable behavior mode expected for this energy
source). As further exceptions, the following do not follow one of three time-series
models: USA Solar, China Hydro, OECD Solar, BRISE Solar, ROW Solar, ROW Wind,
ROW Other Renewables. USA Solar, OECD Solar, BRISE Solar and ROW Solar are
assumed to be identical to China Solar. China Hydro's model parameters are modified to
account for the hydropower resource availability in China. Similarly, the parameters of
ROW Wind and ROW Other Renewables are manually set, as none of the models
yielded reasonable results.
In Table A1, we present trend types and parameters we used for each region and energy
source to forecast capacity additions. The equations are of the following forms.
Linear: pi +p2 Xt
Quadratic: p: +p. Xt +p3 xt?
Exponential: Cap.A dd.(2015) x (1+ p; +p2 xt)
where t is time in calendar years.
10. Total Energy Demand (Figure 5) is externally determined for five regions. (See the
text for an explanation of how it is derived from assumptions presented in A ppendix 2).
11. Forced Retirements are based on the difference between total Energy Demand
and total Energy Supply for the previous year. We assumed that only Oil, Coal and Gas
capacities may be forcefully retired, with that order or priority. We further assumed that
share of capacity that may be retired is capped at 0% for 2015, 2.5% for 2016, 5% for
2017 and 7.5% for 2018 and the following years.
The 34th International Conference of the System Dynamics Society, Delft, Netherlands, July 17 — 21, 2016.
Table A1. Trend types and parameters (A Ivik et al, 2015)
. Energy Source
Region Coal Oil Gas Nuclear | Hyciro Solar Wind Other
Type | Linear | Linear | Quadratic | Linear | Linear | Quadratic | Linear | Exponential
pi | 1069497] 28.95095| -579470] 287.2145] 372.3615| 6052.09] | -657.134| 55.8776
USA TE, | -053168| 0.00085] s7aaoal -o.141| -o.1a07| -e0.475e| 0.320124) 0.02776
Ps =0.14254 0.015108
Type | _Linear [Exponential | _ Linear Linear | Exponential | Quadratic | Linear _ | Exponential
|p 117024] 3.72221] -2227.31] -342.85| 10.38608| 60521.09| -1750.08] 106.9561
China TE, | -87e801| -0.00882| 1.115932| 0.171750] -0.00513| -e0.475e| 0.873374 0.05304
Ps 0.015108
Type | Linear [Linear [Exponential [Linear | Linear | Quadratic | Linear _ | Exponential
p: | 361.4341) 2768.838| 2797254] 1098.621| -134.418| 60521.09| 668.178] _32.37536
OECD |, | -017506) -1.37303| -0.01308/ -0.54621| 0.069007! -e0.a758| 0.3353a1| 0.01608
Ps 0.015108
Type | Linear [Linear | _Linear Linear | Linear | Quadratic | Linear _ | Exponential
p: | -275081| -1738.89| 2470818] -158.136| 1140571) 60521.09| -522.496| 3.91629
BRISE || ise1sia) o.se7ei2| -1.20072| 0.080168) 0.56447] 60.4758] 0.260941] 0.01185
Ps 0.015108
Type | Quadratic [Linear | _Linear Linear | Linear | Quadratic | Linear _ | Exponential
pi | 1086055] -826.3| 149.7825] 18.99218| 159.8248] 6052.09] 1760.08 10
ROW (|, | -107.942| 0.424367| 0.06098] 0.00931] -0.0776| -e0.47se| _0.873374| _ -0.0049
ps | 0.026822 0.015108
Appendix 2
350
300
250
€ 200
=
8 150
100
50 |
0 ——— — — 1
1970 1990 2010 2030 2050
—USA —OECD -——CHINA -——BRISE ——ROW -——Word
Figure A1. Energy intensity forecasts for the “Trends” scenario.
The 34th International Conference of the System Dynamics Society, Delft, Netherlands, July 17 — 21, 2016.
1970 1990 2010 2030 2050
—USA —OECD ——CHINA -——BRISE -——ROW -——Word
billion people
9.0
8.0 4
7.0
6.0 4
5.0
4.0 4
30 sseeecert q
2.0 a ail
a
0.0
Figure A 2. Energy intensity forecasts for the “2°C” scenario.
1970 1990 2010 2030 2050
—USA —OECD ——CHINA ——BRISE -——ROW -——Word
Figure A 3. Population forecasts used in both “Trends” and “2°C” scenarios.
The 34th International Conference of the System Dynamics Society, Delft, Netherlands, July 17 — 21, 2016.
thousand $ / person - yr
50 5
45 |
40 ;
35 |
30 5
20 5
15 +
10
54
0 —+ T — 1
1970 1990 2010 2030 2050
—USA -——OECD -——CHINA -——BRISE ——ROW -——World
200
180 - a
160 - =
140 - a
Figure A4. Productivity forecasts used in both “Trends” and “2°C” scenarios.
5, 120 | at
= 100 - ee
Aas -
F 80
20
aS
1970 1990 2010 2030 2050
—USA —OECD ——CHINA ——BRISE -——ROW -——Word
Figure A5. GDP projections for both “Trends” and “2°C” scenarios.
