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Table of Contents
Modeling innovation-based approaches to climate mitigation
Wolf Dieter Grossmann’, Lorenz Magaard’, James Barney Marsh*
‘Centre for Environmental Research Leipzig, Germany, wolf@ grossman.de
?School of Ocean and Earth Science and Technology, University of Hawaii,
Lorenz@ hawaii.edu, Honolulu, USA
Address of corresponding author
3James Bamey Marsh
College of Business Administration
University of Hawaii at Manoa
Honolulu, Hawaii; 96822
Phone: 808-956 6656
barney@ hawaii.edu
Abstract
Mitigation, decrease of greenhouse gases, is often regarded as expensive and as a major
hurdle to innovation and economic development. Here we describe a systems model that
allows assessment of integrated policies for mitigation and economic development. It is
highly likely that such policies may yield gains instead of costs, up to a considerable
decrease of present emission. This model, and this expectation, is based on accepted
knowledge regarding the costs of mitigation. The model describes interrelationships
between three complex realms that are at the heart of innovation: human knowledge, the
economy, and new key people with knowledge of a basic innovation. The success of real
policies normally depends on their appropriate, integrative, and simultaneous approach
to all three realms. Development and assessment of such policies might help overcome
the present deadlock in mitigation. Through them it should become possible to decrease
risks in innovation and to learn to utilize innovation simultaneously for economic growth
and for decreases of emissions of greenhouse gases. But appropriate integrated policies
are complex to develop and assess. The regionalized global model on innovation and
mitigation described here is a step toward facilitating this process.
Key Words
Mitigation, innovation, information society, ancillary benefits, regionalized global model
1. Innovation-based mitigation
Assuming the Kyoto Protocol is stalled in global politics, it is incumbent on science to
explore methods that are suited to bring together the opposing factions. Obviously, no-
regret policies, which decrease greenhouse gases and, simultaneously, bring economic
gains, should be good candidates for fairly general acceptance. There is evidence that the
building blocks of such win-win policies are waiting in the wings. According to a
thorough overview by the International Panel on Climate Change (Metz et al. 2001),
there is a large and growing reservoir of unexploited technologies, management methods,
products, & production processes that could cut GHG emissions up to 50% with potential
net gain or minimal net economic cost. But startup requires extensive investment in
innovation, knowledge, specialized capital equipment and in key knowledge workers to
guide the processes. As is so often the case, the costs and the benefits are not allocated
equitably so as to provide incentives to undertake the necessary investments. Innovation
nomnally involves high risks and high returns, with the risks and costs being quite certain
and upfront and the returns being uncertain and accruing, if at all, only in the distant
future. Furthermore, much of the benefit may accrue not to the firms that make the
investments but to the population as a whole. On the other hand, investments in
innovation can yield significant economic gain both to the investors and to the public.
Similarly, recent overviews show that those economic sectors that innovate most, and
have highest gains in profitability are those under the highest competitive pressure. Such
pressure can be stronger than the inherent risks of innovation, whatever the noble
promises of decreased GHGs may be. Missing is a tool enabling a more thorough
assessment of innovation’s complexities and potentials to guide markets toward both
private and social/environmental objectives. Systems modeling is an ideal tool to support
such endeavors.
If GHG abating innovation is promising, current US levels of real fixed investment,
at $ 2.7 trillion per year, are encouraging. Of course the huge uncertainty regarding
climate change, added to the enormity of these investments indicate that much research is
needed as to how to piggyback GHG decreasing innovations on them. At present, some
of these investments decrease emissions, others increase them, and still others are neutral.
Overall, specific emissions have decreased in the last 20 years. But this gain has been
more than offset by rapid economic development, in particular in China, India, and other
Asian countries. Rising emissions from such large countries put them at odds with the
developed world. Developing countries view new treaties as threats to their economic
progress. Clearly, market based approaches that utilize innovation for mitigation and
economic gain are opportune and necessary.
For the research we use a family of integrated socioeconomic models on economic
and social innovation that describe the present transition to an information society. These
models have been developed and used for policy development and assessment to support
regional and urban development, e.g. Grossmann 2001, 2002.
Starting with these models, in principle we need to invoke coupling of climate and
socio-economic models in systems that would be genuinely interactive. The idea of
coupling socio-economic and climate models is not new. For example, Hasselmann
(1990, 1999) or Hasselmann et al. (1997) introduce coupled models but, for lack of
sufficient consideration of the innovations of the new economy, concentrate only on
2
minimizing costs of adaptation and mitigation (abatement). We now concentrate on how
to model large-scale innovation of the type that is behind the present emergence of an
information society. There is not yet much research in this area (Weyant, 2000).
2. Major elements in innovation
During the last two centuries, at least, large-scale innovation (“basic innovations”) came
out of close interactions between three realms: economy, key people and knowledge. As
the ISIS-model (Information Society Integrated Systems Model) describes the most
important interactions between these three realms, this is a tool to explore, devise and
assess policies not just for economic and social development - the original use of the
ISIS-models - but also innovation that decreases emissions of GHGs.
Experience shows that successful policies address all three realms. Policy failure is
frequently attributable to addressing just one realm or one of its aspects. In Systems
Dynamics this result is well established, described already by Jay Forrester in his 1969
Urban Dynamics (counterintuitive behavior of complex systems). It was also established,
in macroeconomics, in the Nobel Prize winning work of Robert Mundell which helped
national economies emerge from the painful quagmire of stagflation. What often - not
always - works well is bundles of policies for all three realms, bundles which are so well
tuned that side effects of one policy are compensated by other policies. For example,
policies that support economic development of moder industries create interesting jobs
thus attracting key people. Their migration into well-developing regions, in turn, creates
bottlenecks in housing. This phenomenon caught considerable attention in the recent
bubble phase preceding the crash of the dotcoms. The time lag between the increase in
demand for housing and its construction creates a bottleneck often of significant
proportions. Furthermore, housing construction eats into the natural landscape, degrading
possibilities for outdoor recreation, and thus decreasing regional quality of life. This
example illustrates how policies have to be bundled to address all such issues
simultaneously.
Innovation is risky and sensitive to obvious and subtle factors. Hence, innovation can
be encouraged and supported. In the last 15 years research has made huge progress in
how to provide incentives and frameworks to make innovation happen and how to make
it succeed. One of the outstanding results of this research is in understanding the need for
well-composed support groups that accompany the original inventor from invention to
success in the final test, that is, in the markets. Regions could do much to vastly improve
their success in benefiting from innovation. Some companies have recently learned to be
exceptionally good in providing a “see-it-through until ultimate success-environment” for
their most creative people. Other companies have learned how to make large groups of
people much more creative. For example, Jiirgen Fuchs, who was behind the tum-around
of Lufthansa in the 1990s, created his concept of viable enterprises. Fuchs’ writings (e.g.
Fuchs 1994) are in German, unfortunately, and are not available in English.
3. Three-Landscape Approach to Innovation
New groups of basic innovations are nurtured through evolving sets of the three
realms mentioned above, i.e. new key people, new knowledge, and new economy. We
name these particular realms “landscapes”, because, like landscapes, they are complex
and multifaceted, an image which is also implied in terms such as mindscape,
knowledgescape, and so on.
Historically, one example of basic innovations centered on the first effective steam
engine and brought the industrial revolution in the early 19" century. Another group of
basic innovations relied on electricity and carbochemistry and brought modem industrial
society, starting at around the 1870s. A third group revolutionized mobility since the
1920s and 1930s, in particular cars and trucks, airplanes, radio, and TV. One result is
globalization in tourism and trade, and global concem about grave human or
environmental problems. At present a group of new basic innovations is driving
development towards informatization of lifestyles, products and economy. These basic
innovations utilize vastly extending capabilities coming from processing and transmitting
large amounts of information, so that information now becomes an industrial raw
material for thousands of revolutionary products. Examples are multimedia, the Internet,
the use of the information in genomes, or the deciphering and utilization of proteins. This
development has many names, e.g. information society, none of which is fully
appropriate. We are at the beginning of a societal, economic, political and individual
transformation on a global scale and can at present not well anticipate its major
outcomes.
In the present socio-cultural economic transition, a set of three new landscapes -
“new” key people, new knowledge and “new” economy - is cooperating and competing
with the established set of three landscapes - i.e., “established” key people, “mature”
knowledge and mature industry. Interactions between the old and the new sets of
landscapes affect and transform the physical environment. The physical environment,
ultimately, is the carrier of these interacting sets of landscapes. Some aspect of these
interactions is our topic here, namely an innovation-based approach to mitigation.
The simplest model of these transforming processes, then, consists of two sets of
three landscapes, embedded in their physical environment, with links to emissions of
greenhouse gases. Some of the major components of such models are shown in the next
figures and are explained below. These belong to the family of ISIS-models, used in co-
operation between social and natural scientists. The names in the models have been
chosen so that they are also suitable for mathematical equations, which are preferred by
most natural scientists. The recent trend in variable names in modeling and programming
has been long, self-explanatory names, which, however, would not be suited for
equations. System models for use by both, social and natural scientists need to make a
compromise in variable names.
SO NtSO™N
+ Figure 1: Causal loop diagram of
Key people (+) Economy (4) Knowledge feedback relationships between the three
N/ A landscapes
Explanation of Figure 1: There are mutually positive effects of all three landscapes on
each other. Mathematically it suffices to have these connections through just one
landscape, here the economy. Naturally, more key people also produce more knowledge
even without a connection through the economy; and vice versa, if there is more
knowledge, more key people are needed to master this knowledge and to apply it.
ce)
KPM incr Ml gr KH
MIRI mRI 63
gr RI =
KHM gr size
Ml gr mRI
sizem KHM grRI
KHM gr -
oe m
KPM MI KHM RI
rRI KHM init
ML init rR
KPM obsol RI cal
=~ in Mossel Ml life exp KHM life exp KHM obsol RI
i nRi nRli
ie
Figure 2: Relationship between the three realms for the mature sector
a
Explanation of Figure 2: Shown here is the mature sector of a 3-Region global model
for mitigation through innovation (RGM, an ISIS-model). Shown is a part of Region I,
which comprises the one billion people of the earth who live in highly developed regions.
The three state variables are each modified by its own processes of creation and
disappearance, or “obsolescence.” The left side of the figure depicts key people, the
middle mature industry, and the right mature know-how. MI stands for mature industry,
RI for Region I. KPM denotes key people in mature industry, KHM stands for know-how
in mature industry, etc. With this nomenclature, KHM RI is know-how in mature
industry of Region I. The single letter “n” stands for “normal”, the normal value of a
variable, ie. a constant. Key people in mature industry increase in numbers
(KPM_ incr RI) through training. The flow variable “KPM incr RI,” therefore, is the
increase in the number of key people for mature industry in Region I. Their knowledge
has a limited lifetime (expressed by the constant “KPM obsolescence normal Region I”)
and these people therefore leave the industrial system through the flow variable
“KPM_obsol_RI,” unless they undergo continuous retraining. Likewise, the flow
variables “MI gr RI” (mature industry growth, Region I) and “MI obsol_RI” (mature
indstry obsolescence Region I) control the development of mature industry, and the flow
variables “KHM_gr RI” and “KHM_obsol_RI” control the development of know-how of
the mature industry.
Several feedback loops of mutual influence link these subsystems. They are
controlled in the well-known way of System Dynamics by multipliers so that these three
subsystems can only develop well if in harmony with each other. The multipliers
(functions) are of s-shaped form, as efficiency of any particular input is low for low
values (beginning of s-shaped curve), and has a saturation (right side of s-shaped curve).
Data for such systems are available from some standard and not so standard sources. For
example, the unit cost per job or the fraction of leading people in the overall workforce,
are standard from statistical offices. As an example of non-standard sources, see the
research on innovation by Michael Porter and colleagues (e.g. Porter & Stem 2001) on
the importance of innovation and how to measure it, using the number of patents. To
avoid measurement problems and ambiguities, we divide the state variable for know-how
by its initial value which eliminates dimension so that only the growth factor remains.
The sub model for new economy has the same structure as the sub model for mature
industry, but the data are different. For example, in reality, the importance of key people
for mature industry is considerable, but their training and curriculum have been well-
established since the 1930s. Also, key people comprise only a small fraction of the
overall workforce in mature industry, so that their availability is not so much a limiting
factor. But in the new economy, new key people are a large fraction of the workforce and
the most important bottleneck in slowing growth of new economy, a fact which is
indicated by the stiff global competition for such people (Stern and Porter 2001, Florida
2002, Grossmann et al. 1997, findings by HUD 1997, or Schumpeter 1939). With the
growing importance of knowledge, technology and information, a model on innovation
policies needs to include these factors, and with them, the “creative class” (Richard
Florida). In mature industry, due to rationalization and loss of jobs, there is usually a
good supply of key people available to the new economy. This submodel of new
economy, here and in reality, is not seriously limited by labor supply. Because of
globalization, large numbers of workers are available in rapidly developing countries like
China and India, a positive aspect of the outsourcing controversy. Like Stern and Porter,
2001, we have not included normal labor as a productive or limiting factor. Such a
system of equations would have been without relevance for the economic reality of 15
years ago. With ongoing economic growth and maturity of the present new economy,
amplified by aging populations in most countries, labor will again become a limiting
productive factor.
To deal with implications of aging populations on economic change and emissions of
GHGs, we have included a global population subsystem with 8 age groups which, in
accordance to projections by UNESCO and other respected groups, shows a peak in
population number in 2050 at about 9 billion people. This is in the upper third of
projections, which anticipate peaks ranging from 7.5 billion to 9.5 billion people.
4. A Two-Sector Approach to Economic Transformation
Recent reviews of economic development in the past 15 years confirm that there is a
vigorous, large-scale emergence of a new economy, and that this new economy is
transforming established, mature, industry (Farrell, 2003, for background material see
e.g. Enriquez and Goldberg 2000, Castells 2000, Margherio et al. 1998). From the
viewpoint of economics, this is a two-sector situation of economic transformation. With
this viewpoint, the economy furnishes ample knowledge on how to model these
relationships. The present authors have described the process, in another paper as follows
(Grossmann et al., under revision): “First, hitherto highly concentrated accumulations of
economic assets and economic power, as well as geographic agglomerations and long
established path dependencies, are “melting” under the influence of networks and
information technologies (Jensen 2000). As derived incomes are redistributed, social
systems can become, albeit imperfectly, more equitable and more democratic. Among the
results are GDPs, particularly in the highly developed countries, that circulate more
rapidly, that are significantly less heavy industry- and more information-intensive (Porter
& Stern 2001). Physically and electronically integrated global markets are the productive
origins of these GDPs. These often-observed trends may themselves be sources of
mitigation of, and adaptation to, potentially negative impacts of climate change.
Information, offsetting smokestack intensity, is an obvious case in point, which has
driven down relative, but not so much absolute, energy use. Projections from the IPCC
would imply that much more needs to be done. ... An important tool here is an adaptation
of New Economic Geography (NEG) studies to channel the dynamics to present and
future locational trends (Fujita et al 2000). Other tools of importance include the results
of decades of rich international business and economic analyses of corporate structure,
competitive advantage, innovation & entrepreneurship, marine and ocean economics, and
the like (Romer 1990). Enormous trade-offs prevail. The “melting” mentioned above and
its concomitant “lightening” of GDPs through information inputs are only partial.
Agglomerations, clusters, and heavy industries continue to exist, in part due to inertia and
economic demands, but in larger part due to increasing returns to clusters. Goods
production has globally grown considerably whereas the global number of jobs in
manufacturing has declined by more than 20 million between 1997 and 2002, Colvin
2003.” (End of citation).
The RGM-model therefore has, for each region, a new sector and a mature sector.
ISIS-models for regional development (Grossmann 2002, and Grossmann et al, under
revision), have manifold relationships between mature and new sectors, which allows an
extension of the RGM-model.
5. A Three-Region Approach to Global Modeling
The speed and transformative power of innovation is very different for different parts
of the globe. The developed, rich countries see, although they are highly developed, a
massive transformation of their economies. This is Region I in the RGM-model; the
number of inhabitants of Region I is at present 1 billion people. There is a second group
of countries, those which are less developed but are developing more or less rapidly. This
is Region II in our model, with 4 billion people. For some of them, development has
recently accelerated. China is an obvious example, with its continued economic growth
rates of 10% - 12%, or India with growth rates of 8% - 10%. For these rapidly developing
countries the changes are even more pronounced and faster than for the rich countries.
Unfortunately, not all countries belong to these two categories. There is a cumbersome
third group, those, which are not really developing, constituting Region III, with 1 billion
people. Any realistic modeling of innovation, therefore, needs at least these three regions.
Each of the three regions in RGM has two sets of equations describing mature industry
and new economy, with three landscapes each.
6. Policy Section of the RG M-Model
Before navigating the detailed and painstaking research path to realistic policies,
RGM strategy entails realm specific master policies to test the efficacy of possible
bundles of realistic or applied policies. RGM’s master policies are simple, not realistic,
with parameters not specified in advance. Nevertheless, they provide a transition to an
analysis of the possible effects of realistic bundles of policies. If we find suitable master
policies or combinations of master policies, defined in terms of the realms in which they
are active, by their strength, beginning and duration of policy influence, then work can
begin to devise realistic bundles of policies. This may include optimization of the model
as done for policy development with one ISIS model by B. Grossmann 2002. These
realistic bundles would induce the same effect with respect to strength of influence, but
would have the added benefit of being capable of implementation. RGM has a proven
record of devising bundles of realistic policies. Examples of policies that worked in
actual applications appear in a sub-sector of RGM. The policy section of RGM shows
typical policies for all three landscapes.
Figure 3 shows policies of major importance with respect to quality of life. There
seem to be both, policies of almost global relevance and applicability and policies which
are a must for specific regions but with little global appeal. That is why bundles of
policies have to be composed individually for each region. Learning is possible, but not
all lessons leamed apply everywhere. Figure 3 gives examples which, in a suitable
combination, hopefully allow to translate the master policy for quality of life for key
people into reality. All issues in that figure have been carefully collected from
applications by the authors as well as from successful development all over the world.
Preschool facilities
widely available Tourism of
Excellent highest quality
schools
Quality of life =
' ., from tourism
Quality of life Kppealing
Quality of life ae > high quality
' 7 architecture
from schools = Quality of life
from architecture
Low crime Quality of life i
rate from safety
Quality of life QL from
a oe r new key people outdoor recr m
Outdoor
recreat
Sparetime =
offers { QL from QL from
sparetime m Housing
Figure 3: Example for realistic policies with additional input from Hamburg Chamber of
Commerce 2003
Some issues, like quality of life from architecture, are indicators for other, more
profound characteristics of a region. The potential attractiveness of outstanding
architecture was learned just recently through striking examples such as the new, famous
museum in Bilbao which has become a magnet for visitors from all over the world. What
is appealing for key people is usually as well appealing for the rest of the population;
only that key people with their economic relevance can serve as a more convincing
reason to increase quality of life. And what is attractive for visitors is often also attractive
for the local population.
A major focus of policies is to enhance the efficacy and power of the three legs on
which desired outcomes depend: key people, knowledge and the economy. In each case,
individuals, often highly skilled, do the work involved. We know from experience that
people must not only be trained, they must be kept happy. They must be able to
participate in and benefit from the information society as well as enjoy an invigorating
and restorative life style. Quality of life is important to attract and keep key people, but it
does not breed new key people.
Hence, policies for the development of key people involve, first & foremost, training
with new skills, and for different levels of skills. Key people might be further encouraged
through interactive special interest groups, which policies can encourage. It is now clear
that educated people demand an improved quality of life, in particular through highly
developed arts sectors, leisure & outdoor recreation, and ecological revitalization of
natural environments (Levitt, 2002, Grossmann et al 1997). Leisure (or sparetime,
everything created, built, offered by people such as restaurants, museums, arts,
exhibitions) and outdoor recreation (everything related to nature) seem to be, may be
globally, the two most important issues in quality of life for new key people. However,
keep in mind that young key people get older, have family and children and care for more
things than just their amusement. That is why the health sector, schools, safety and so on
may not be mentioned prominently in questionnaires but turn out to be highly important
in reality. Usually, a good group of local experts can rapidly agree on the relative
importance of the factors of quality of life in relation to each other. Finally, policies must
encourage creativity, innovation and the entrepreneurial spirit. This begins with such
down to earth issues as good availability of venture capital, a regional administration that
is fast and supportive, clever laws for bankruptcy which do not punish the entrepreneur
after a first failure with ineffectiveness for the rest of his or her life and so on, see Figure
4 and Figure 5.
Chapter 11
New Stock equiy
Markets
Tax
structure One stop Figure 4. Factors that are
bureaucracy important for new start-
ups
Program for economic
innovat environ
Venture capital
Econ national
innovat environ
Educate for Readiness Climate for
for new thoughts Breakthroughs
Figure 5. Factors that
support creativeness
Organize
Social Support
Climate for
Playfullness
Organize
Psychol Support
C) QL for KP from
creative climate
Policies must enable and facilitate the creation of knowledge, another of the three
landscapes. Figure 5 addresses the creativity aspect of this landscape; the “support” in
10
this figure is for creativeness. Institutionally, this involves the growth and nurturing of
excellence in first class universities.
Excell libraries
Universities & Web use ADCs Figure 6. Factors that contribute to
the generation of new knowledge,
see text. ADC: Advanced
Program Special development center. Web use:
Interest Groups support people in advanced,
sophisticated use of the Web.
Schools
NE KH
location m
We have been hesitant to provide
Figure 6 because it may be
misleading. We do not need the same old curricula all over again, and the same old
research only now under a more fashionable heading. This is what seems to be done
almost universally, when new curricula are called for; look just at the fate of System
Dynamics with its difficulties in its very prolonged phase of fledgling. This figure looks
pretty conventional and, to most people, reassuring. What is necessary is break-through
knowledge how to do new things with “tons of information and light-years of networks”
(Ruediger Warnke, a German consultant and leading thinker in management seminars).
Additionally, a variety of enabling organizations, both government and private, would be
needed to provide support for innovation, grants, official assistance. Equally important,
however, is legislation which enables private, market oriented initiatives and disables
excessive government interference and control.
Finally, policies to nurture the economy are vital. The government must provide a
well-established rule of law, generally ensuring both economic and political freedom.
Fewer laws are often better than more laws, thereby leaving creativeness to the intuition
and decision of people and management of the economy mostly up to the market system.
Nevertheless, the laws must be administered through a government judiciary.
Furthermore, the tax code must be designed so as to minimize its discouraging and
disrupting influences. Property rights, including intellectual property rights must be
supported in the judicial system. There must be adequate and practical laws regarding
bankruptcy. Policies should remove all impediments to the free flow of information and,
in fact, help develop an information infrastructure which is cheap, good, fast, extended
and evolving.
Another group of policies for the economy includes the locational factors of a region or
city. Here again, for the new economy, attractive facilities for leisure, excellent
universities, and a favorable natural environment are of highest importance. Also
important is good access to specialized consulting, job training for the new economy, and
market rents for office spaces. A regional intercontinental airport of high quality has also
become a major support for high-tech companies.
11
7. Modeling policies
Policies for the economy increase or decrease the rate of investment by a factor,
weighted by the importance of the policy. Policies for key people increase or decrease
their training by a factor, and change the normal flux of key people moving to a region or
leaving it, also by factors. Policies for knowledge creation accelerate learning and extend
the scope of research. All of these factors are implemented in the model as multipliers,
which increase the respective inflow into its state variable. The value of the multiplier
depends on the power of the individual policy.
Almost all individual changes through a specific realistic policy are small (see e.g.
Figure 9). Some policies are more powerful than others. The relative importance of
policies is found through consultations with experts and from literature. The experts sort
policies according to their relative importance and organize them by power. Amazingly,
groups of experts from different fields often agree quite well on their ordering of policies.
This approach does not yet include costs of policies. Doing so might give a very
different set.
8. Modelling of greenhouse gas emissions
Each emission rate is connected to an economic state variable, and is dependent on
policies and technological progress. Figure 7 shows how policies, with a lag, decrease (or
increase) emissions of GHGs, depending on the beginning, vigour and duration of the
policy.
Figure 7:
Emissions are caused
by the economy ina
region; here mature
Begin of Ril Ril: policies for
MI GHG decrease
Duration of RII
MI GHG decrease GHG decrease
industries, Region II.
Fc enioare MIRII GHG decrease " eis om
changed by — overall effect ecrn
investments in MERI RII MI specific
technology. level of GHG emi
RII MI GHG
RII MI GHG
GHG RII MI emi i
anit emi spec decr
Explanation of Figure 7: Total emissions of mature industry in this region (here
Region II) are the product of the size of the mature industry (MI RII) times the specific
emissions per capital unit (RII MI specific level of GHG emissions). The specific
emissions are subject to policies with the total effect given by the variable “MI RII GHG
decrease overall effect”.
12
The model run shown in Figure 8 gives overall global economic development (curve
1), resulting from realistic, albeit different, economic growth rates for each of the three
global regions, and the resulting emissions of GHGs in tons. Here, economy and
emissions are closely related (no decoupling) as the assumption is that modemization
does not much decrease specific emissions (which is too cautious and not realistic), and
no policies are used. The assumptions underlying economic development are similar to
those of the SRES A2 scenario and the resulting curve of emissions is also very similar to
that of SRES A2. At a 3% average global economic growth rate, a 1% decrease in
specific emissions in the model gives the typical upper range of SRES-scenarios, Figure 8
(compare with Metz et al. 2001, figure SPM.1, lower left, SPM-1d IPCC SRES A2
Scenarios - please note that they use gigatons C in their scaling instead of tons as in
Figure 8). The group of ISIS-models might therefore become useful in exploring SRES-
scenarios with the aim of benefiting from economic innovation.
1: Econ global total 2: GHG global total
3e+014.
3e+010.
1.5e+014
1.5e+010
0
0
1990.00 2000.00 2010.00 2020.00 2030.00 2040.00
Years
Figure 8: Economic global development and total emission of GHGs (in tons C-equivalent).
The model run in Figure 9 shows the rather low effect of isolated policies on
economic development (curve 2 compared to curve 1 which is the development without
such policies), and the remarkable effect of bundles of policies (curve 3). According to
everything we have leamed in our economic regional studies such a positive outcome
should be possible, and we have seen it in a practical application. (However, rather
systematic efforts by other, highly advanced, groups did not give comparable effects. We
compare such policies with education of a young person: a good education puts him or
her into a better position for a good career, but does not guarantee it.)
13
Economy total: -2-3-4
a 2e40L Ley
1 let0lis
, A
1980.00 1990.00 2000.00 2010.00 2020.00
Years
Tq Econ total 1980-2020 compare
Figure 9: Visible effect of training of new key people on economic development
1: Base run - no policies
2: Isolated policy - little effect on growth
3: Bundle of policies from all four landscaped improves economic growth
4: Bundle as in 3 plus innovation policies against GHGs - no negative effect on growth
Curve 4 is most interesting, as it comes from a the same bundle of policies as in
curve 3 plus innovation policies against GHGs which use ongoing investments. These
policies have no visible negative effect on growth. Figure 10 shows the effect of these
four innovation policies on emissions of GHGs. In curves 1 and 2 the emissions increase
more or less in parallel with economic growth, because there is just the 1% specific
decrease in emissions of GHGs, similar to the past development. Curve 3 shows a
considerable increase in emissions, whereas curve 4 shows the impact on emissions of
GHGs of an economic innovation that also aims at decrease of emissions. Much could be
achieved in a short period of time with little additional costs, both for the economy and
the issue of climate change.
In another study we could show that regional policies can simultaneously increase
economic growth and achieve adaptation to climate change with the net result of an
economic gain (Grossmann et al. 2003). Other authors report similar results, e.g. Metz et
al. 2001, Lovins et al. 1997. Once we have more results on such policies we will use
RGM to determine their effect on economic growth and emissions of GHGs in a global
scale.
14
9. Integrated Policies for Implementable, Effective Solutions
In model runs for the city of Hamburg with an extended version of ISIS for regional
analysis we analyzed the effect of training key people (or of acquiring more key people)
on economic growth. This training accelerates economic growth in theory, but,
unfortunately, not in the long run. As mentioned above, such increase of the number of
new key people causes increased competition for housing, which, in turn, increases out-
migration. We did not expect this result. In our model run, we proceeded assuming the
received wisdom of regional economics that key people will be paid well enough to face
increases in rents. This ability to pay, however, does not exist in the early phases of the
development of a new basic innovation, because in those phases these new key people
need a very large portion of their money to feed their new inventions and new businesses.
Indeed, the city of Hamburg has seen a temporary increase in new key people, with an
ensuing high loss of start-ups to other cities due to the high rents in Hamburg. This
version of ISIS enabled us to shed perception blocking misinformation about events
transpiring around us in Hamburg, things that actually did happen, and to read the
newspaper accounts with greater critical skill. Models are good for such learning.
Ss GHG total -2-3-4
i 2500008
i 1500008
i: 50000
1980.00 1990.00 2000.00 2010.00 20
Years 9:36 AM Thu, Jun
\@eeF 9 GHG total compare / page 2
Figure 10: Shown are emissions of GHGs (index) for the policies shown in figure 9
1: Base run
2: Isolated policy - little effect
3: Bundle of policies for economic growth: Much higher emissions of GHGs
4: Bundle as in 3 plus policies against GHGs - decrease through ongoing innovation
15
As we want to use increased innovation for decreased emissions, by emphasis on a
new economy with lower specific emission rates, we need a tool for integrated policy
assessment. One reason for decrease of emissions could stem from an ongoing global
competition between mature industries and the new economy for the purchasing power
(or income) of regions. As the products of the information-intensive economy develop in
sophistication and attractiveness, they, increasingly, outcompete products which are less
information-rich. The production level of material goods declines, relatively, toward an
unknown minimum, although it most likely will continue to increase in absolute terms. It
is possible that material goods may end up like agriculture, which in many developed
countries overfeeds the people yet accounts for little more than 1% of Gross Domestic
Product. Even if the relative share of material goods drops to as little as 5% of GDP, this
still may mean dramatic emissions. From a viewpoint of GHG-emissions, this relative
share is considerably better than present levels of about 20%. Clearly, this competition
between information products and material goods, is highly relevant for mitigation.
As mentioned above, economic development is very sensitive to the availability of
key people. This is shown for three different rates of availability in Figure 11. The
model’s sensitivity is highly welcome as it corresponds to reality.
NERI: 1- 2- 3-
4e+012.
2e4012.
1990 2000 2010 2020 2030 2040
Years
Rill: New Sector NE Compare /Page 4
Figure 11: Economic development of Region III with different availability of new key
people. Curve 1: standard run; no policies. Curve 2: additional extensive training. Curve 3
might be closest to reality as many key people migrate from Region III to Region I.
16
10. Outlook
We have embarked on an ambitious project to build a tool for integrated policy
development for mitigation through innovation. This involves a family of fairly complex
simulation models, actual applications and consulting, and is bringing together an
interdisciplinary group of researchers from the US, Asia and Europe.
We use the present prototype for testing the importance of different hypotheses. The
prototype serves as a reference model to see what various refinements in elaborated
models will bring. If refinements are simply cosmetic and make little difference, we keep
the models simple by not including them in final versions, of course with appropriate
documentation.
Some model runs were disappointing in that the most well-intended sets of policies
for mitigation were, in the long run, overrun by economic growth. This occurred in spite
of our use of the most environmentally benign technology, with almost total
dematerialization and resulting low specific, but not absolute, emissions of GHGs.
Model development for realistic estimates of costs will be done with the final model.
We expect, as in an application of ISIS-models to the problem area of adaptation
(Grossmann et al. under revision), that effective innovation policies will be possible
which provide considerable economic gains and, simultaneously, curb emissions.
The development of these effective innovation policies would help to overcome the
present political battle, a highly detrimental struggle given grave risks due to climate
change. Effective policies must integrate very different sectors, which complicates the
elaboration and assessment of such policies. Assessment needs an effective tool set,
which then can help in integration over disciplines and administrative courts. The
systems model RGM is a piece in this emerging tool set.
17
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