Measuring industrial production capacity caking account of malfunctions of pr...Global Risk Forum GRFDavos
Hirokazu TATANO1, Yoshio KAJITANI2
1Disaster Prevention Research Institute, Kyoto University, Japan; 2Disaster Prevention Research Institute, Kyoto University, Japan
Estimating Financial Frictions under LearningGRAPE
The paper studies the implication of initial beliefs and associated confidence under adaptive learning. We first illustrate how prior beliefs determine learning dynamics and the evolution of endogenous variables in a small DSGE model with credit-constrained agents, in which rational expectations are replaced by constant-gain adaptive learning. We then examine how discretionary experimenting with new macroeconomic policies is affected by expectations that agents have in relation to these policies. More specifically, we show that a newly introduced macro-prudential policy that aims at making leverage counter-cyclical can lead to substantial increase in fluctuations under learning, when the economy is hit by financial shocks, if beliefs reflect imperfect information about the policy experiment.
The dangers of policy experiments Initial beliefs under adaptive learningGRAPE
The paper studies the implication of initial beliefs and associated confidence on the system’s
dynamics under adaptive learning. We first illustrate how prior beliefs determine learning dynamics
and the evolution of endogenous variables in a small DSGE model with credit-constrained agents,
in which rational expectations are replaced by constant-gain adaptive learning. We then examine
how discretionary experimenting with new macroeconomic policies is affected by expectations that
agents have in relation to these policies. More specifically, we show that a newly introduced macroprudential policy that aims at making leverage counter-cyclical can lead to substantial increase in
fluctuations under learning, when the economy is hit by financial shocks, if beliefs reflect imperfect
information about the policy experiment. This is in the stark contrast to the effects of such policy
under rational expectations.
Measuring industrial production capacity caking account of malfunctions of pr...Global Risk Forum GRFDavos
Hirokazu TATANO1, Yoshio KAJITANI2
1Disaster Prevention Research Institute, Kyoto University, Japan; 2Disaster Prevention Research Institute, Kyoto University, Japan
Estimating Financial Frictions under LearningGRAPE
The paper studies the implication of initial beliefs and associated confidence under adaptive learning. We first illustrate how prior beliefs determine learning dynamics and the evolution of endogenous variables in a small DSGE model with credit-constrained agents, in which rational expectations are replaced by constant-gain adaptive learning. We then examine how discretionary experimenting with new macroeconomic policies is affected by expectations that agents have in relation to these policies. More specifically, we show that a newly introduced macro-prudential policy that aims at making leverage counter-cyclical can lead to substantial increase in fluctuations under learning, when the economy is hit by financial shocks, if beliefs reflect imperfect information about the policy experiment.
The dangers of policy experiments Initial beliefs under adaptive learningGRAPE
The paper studies the implication of initial beliefs and associated confidence on the system’s
dynamics under adaptive learning. We first illustrate how prior beliefs determine learning dynamics
and the evolution of endogenous variables in a small DSGE model with credit-constrained agents,
in which rational expectations are replaced by constant-gain adaptive learning. We then examine
how discretionary experimenting with new macroeconomic policies is affected by expectations that
agents have in relation to these policies. More specifically, we show that a newly introduced macroprudential policy that aims at making leverage counter-cyclical can lead to substantial increase in
fluctuations under learning, when the economy is hit by financial shocks, if beliefs reflect imperfect
information about the policy experiment. This is in the stark contrast to the effects of such policy
under rational expectations.
Del av seminariet "Från kolkälla till kolfälla: Om framtidens klimatsmarta jordbruk"
8 maj 2012, 13.00 - 16.30
Kulturhuset, Stockholm
Kan man planera för mindre utsläpp från jordbruksmarken? Madeleine Jönsson, FAO, om planeringsverktyg för klimatsmart jordbruk.
La "Smart Specialisation Strategy (S3)" è un approccio strategico allo sviluppo economico regionale attraverso un sostegno mirato alla ricerca e all'innovazione (R & I). La S3 rappresenta la base per gli investimenti in R&I attraverso i fondi strutturali nel quadro della nuova politica di coesione e del contributo della stessa alla Strategia 2020 per la crescita ed il lavoro in Europa. Più in generale, la "specializzazione intelligente" implica la generazione di una visione complessiva dello sviluppo regionale attraverso l'identificazione dei vantaggi competitivi, delle priorità strategiche e delle migliori politiche pubbliche a sostegno del territorio.
For more information about the Informed Cities initiative visit http://informed-cities.iclei-europe.org or join us on Facebook at https://www.facebook.com/InformedCities
Infrastructure and Investment Opportunities for Energy Efficiency in BuildingsAlliance To Save Energy
Vice President for Programs Jeff Harris (jharris@ase.org) discussed energy efficiency measures in new and existing buildings, as well as cross-cutting techniques for achieving maximum advantages. Jeff’s work focuses on U.S. and international energy efficiency policies for buildings, appliances, and utilities, and market transformation through public sector leadership.
It’s no wonder that smart meter rollouts have skyrocketed with supporting business case findings such as ComEd customers saving potentially $2.8 billion on their electric bills over the 20-year life of the smart meters. Largely due to the aggressive U.S. effort to modernize its electric grid pros and cons (for example PG&E will now offer ‘opt out option’) for smart meters are still aggressively being debated; nonetheless the number of smart meters installed in the U.S. has ballooned over the past several years – with just over fifty utilities deploying the bulk of the investment. Zpryme analyzed data from the EIA in an effort to not only breakdown smart meter deployments by utility but also to zero in on the drivers that will bridge the U.S. energy divide.
Del av seminariet "Från kolkälla till kolfälla: Om framtidens klimatsmarta jordbruk"
8 maj 2012, 13.00 - 16.30
Kulturhuset, Stockholm
Kan man planera för mindre utsläpp från jordbruksmarken? Madeleine Jönsson, FAO, om planeringsverktyg för klimatsmart jordbruk.
La "Smart Specialisation Strategy (S3)" è un approccio strategico allo sviluppo economico regionale attraverso un sostegno mirato alla ricerca e all'innovazione (R & I). La S3 rappresenta la base per gli investimenti in R&I attraverso i fondi strutturali nel quadro della nuova politica di coesione e del contributo della stessa alla Strategia 2020 per la crescita ed il lavoro in Europa. Più in generale, la "specializzazione intelligente" implica la generazione di una visione complessiva dello sviluppo regionale attraverso l'identificazione dei vantaggi competitivi, delle priorità strategiche e delle migliori politiche pubbliche a sostegno del territorio.
For more information about the Informed Cities initiative visit http://informed-cities.iclei-europe.org or join us on Facebook at https://www.facebook.com/InformedCities
Infrastructure and Investment Opportunities for Energy Efficiency in BuildingsAlliance To Save Energy
Vice President for Programs Jeff Harris (jharris@ase.org) discussed energy efficiency measures in new and existing buildings, as well as cross-cutting techniques for achieving maximum advantages. Jeff’s work focuses on U.S. and international energy efficiency policies for buildings, appliances, and utilities, and market transformation through public sector leadership.
It’s no wonder that smart meter rollouts have skyrocketed with supporting business case findings such as ComEd customers saving potentially $2.8 billion on their electric bills over the 20-year life of the smart meters. Largely due to the aggressive U.S. effort to modernize its electric grid pros and cons (for example PG&E will now offer ‘opt out option’) for smart meters are still aggressively being debated; nonetheless the number of smart meters installed in the U.S. has ballooned over the past several years – with just over fifty utilities deploying the bulk of the investment. Zpryme analyzed data from the EIA in an effort to not only breakdown smart meter deployments by utility but also to zero in on the drivers that will bridge the U.S. energy divide.
Insead Alumni Energy Network 22nd October 2011 by Benjamin WarrBenjamin Warr
What role does energy, and specifically oil play in the economy? What impact on growth can we expect a decline in oil production to have? When is the decline in production likely to happen? What can we do to mitigate the worst impacts?
Economic Growth Models and the Role of Physical ResourcesBenjamin Warr
Conventional economic theory assumes that technological progress is exogenous and that resource consumption is a consequence, not a cause, of growth. The reality is more complex. In effect energy consumption is just as much a driver or economic growth as it is a consequence.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Elevating Tactical DDD Patterns Through Object Calisthenics
Warr 3rd Iiasa Titech Technical Meeting
1. 3rd IIASA-TITECH Technical Meeting
21st – 22nd September 2003, Vienna
Center for the Management of Environmental and Social Responsibility (CMER)
INSEAD
Boulevard de Constance
Fontainebleau
77300
http://benjamin.warr.free.fr
Resource EXergy Services Forecasts
REXS-F
An economic forecasting model with quasi-logistic technical
progress
28/04/2003 1
2. Overview
– Reminder of REXS model
• Historical trends in resource use
– Energy Intensity
– Conversion Efficiency,
and economic output.
– Forecasts of output varying future trends of
• Energy Intensity
• Conversion Efficiency,
for JAPAN and the US
28/04/2003 2
3. Reminder of REXS economic output
module (ICT components optional)
Exe rgy
Labour Capital
Serv ice s
Linex
parameter a ICT Fraction of
Gross Output Capital
Linex
parameter b
ICT Capital
Linex Growth Rate
Parameter c
Cumulativ e
Production
Monetary Monetary
Output
28/04/2003 3
4. Common practice
Y t = Q ( A t , H t K t , G t L t , F t R t ),
Y t = A t (H t K t ) (G t L t ) (F t R t )
α β γ
Yt is output at time t, given by Q a function of,
• Kt , Lt , Rt inputs of capital, labour and natural
resource services.
• α, + β + γ = 1, (constant returns to scale assumption)
• At is total factor productivity
• Ht , Gt and Ft coefficients of factor quality
28/04/2003 4
5. The production function can be either CD, o
LINEX
L + U L
Yt = U expa 2 −
+ ab − 1
K U
For the US a = 0.12, b = 3.4 (2.7 for Japan)
Corresponds to Y = K0.38 L0.08 U 0.56
• At ‘total factor productivity’ is REMOVED
• Resources (Energy & Materials) replaced by
WORK
• Ft = energy-to-work conversion efficiency
• Factors ARE MUTUALLY DEPENDENT
• Empirical elasticities DO NOT EQUAL COST
SHARE
28/04/2003 5
6. The Virtuous Cycle driving Historical Growth
Product R&D Substitution of
Improvement Knowledge for Labour;
Capital; and Exergy
Process
Improvement
Substitution of
Exergy for Labour
Lower Limits to
and Capital
Costs of
INCREASED REVENUES Production
Increased Demand for
Final Goods and Services
Economies of Lower Prices of
Scale Materials &
Energy
28/04/2003 6
7. Output – validation of full model for the US
Simulated and empirical GDP, USA 1900-2000
25
simulated
empirical
20
normalised GDP (1900=1)
15
10
5
0
1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
year
28/04/2003 7
8. Trend of “Dematerialisation” – a constraint
on future productivity ?
Simulated and empirical primary exergy intensity of output,
USA 1900-2000
1.2
1
0.8
r/y (1900=1)
0.6
0.4
0.2 empirical
simulated
0
1900 1910 1920 1930 1940 1950 1960 1970 1980 1990
28/04/2003 8
year
9. Aggregate conversion efficiencies have improved
significantly but are they slowing?
0,18
0,16
0,14
technical efficiency, f
0,12
0,1
0,08
0,06
empirical (U/R)"
0,04
bilogistic model
0,02
0
25 695 1486 2660 4677 7113
cumulative primary exergy production (eJ)
28/04/2003 9
Source Data: Ayres, Ayres and Warr, 2003
10. Could the future rate of technical efficiency
growth also be a constraint?
Rate of change of aggregate technical efficiency
of primary exergy conversion, USA 1900-2000
0.0025
0.002
rate of change
0.0015
0.001
0.0005
10 yr moving average
0
0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00
28/04/2003 10
cumulative production (1900=1)
11. REXS Projections of future output
PROJECTIONS A
Altering the future rates of the energy
intensity of output
The average decay rate of the exergy intensity of
output (R/GDP) for the period 1900-1998 is 1.2%
The simulations involved increasing or decreasing
this parameter from 1998 onwards, while keeping
the values of all other parameters fixed.
PROJECTIONS B
The future growth rate of technical efficiency of
exergy conversion is uncertain. We tested 3
alternatives, keeping the ‘demat rate’ at 1.2%s
28/04/2003 11
12. Trends in technical efficiency for other
countries
Aggregate conversion efficiency of commercial
fuel exergy to useful work,1960-1998
0.30
0.25
0.20
percentage (%)
0.15
0.10
0.05
France Germany Japan UK US
0.00
1960 1965 1970 1975 1980 1985 1990 1995
year
28/04/2003 12
13. Possible trajectories for future technical efficiency (US)
P o s s ib le t r a je c to r ie s o f te c h n ic a l e ffic ie n c y w ith p r im a r y
e x e r g y p r o d u c tio n e x p e r ie n c e , U S 1 9 0 0 -2 0 5 0
0 .3 0 d o u b lin g o f e x p e r ie n c e
E m p ir ic a l tr e n d f r o m 1900
to 2 0 0 0 a n d c u m u la tiv e p r im a r y
e x e r g y p r o d u c tio n
h ig h 130%
0 .2 5
m id 120%
lo w 115%
0 .2 0
technical efficiency
0 .1 5
P la u s ib le
im p r o v e m e n t
o n c u rre n t
e ffic ie n c y o f
0 .1 0 e x e rg y
c o n v e r s io n
0 .0 5 S im u la tio n re s u lts u s in g
th e p la u s ib le tra je c to r ie s o f
te c h n ic a l e ffic ie n c y g ro w th
a s a fu n c tio n o f c u m u la tiv e
p r im a ry e x e r g y p r o d u c tio n
0 .0 0
0 2000 4000 6000 8000 10000 12000 14000
28/04/2003 year 13
14. Forecast Gross Output (GDP), US 2000-2050
Forecast Gross Output REXS F US100
45 Simulation results using HIGH
the plausible trajectories of Initial ~3% growth rate, for 130%
technical efficiency growth target increase in technical
as a function of cumulative efficiency.
33.75
primary exergy production
22.5 MID
Initial 1.5% growth rate for target
120% improvement in efficiency.
11.25
LOW
Shrinking economy at rate of
2 - 2.5% after 2010 if the target
0 technical efficiency is only 115%
1900 1918 1936 1954 1972 1990 2008 2026 2044
year
greater than the current.
GDP (1900=1)
empirical
low
mid
high
28/04/2003 14
15. Marginal productivity of exergy services (work)
Graph for Marginal Productivity of Exergy Services
1
Increasing marginal productivity of
exergy services as technological
progress increases.
0.85
Faster rate of marginal productivity
growth with slower rates
of technological progress.
0.7
Once the economy slows the
marginal productivity of capital
is negatively affected.
0.55
highest
0.4
1900 1920 1940 1960 1980 2000 2020 2040
Time (Year) Simulation results using
Marginal Productivity of Exergy Services : flin04 the plausible trajectories of
Marginal Productivity of Exergy Services : flin03 technical efficiency growth
Marginal Productivity of Exergy Services : flin02 as a function of cumulative
Marginal Productivity of Exergy Services : flin01
Marginal Productivity of Exergy Services : Empirical Data primary exergy production
28/04/2003 15
16. Marginal productivity of capital
Graph for Marginal Productivity of Capital
0.04
Slower growth in the marginal
productivity of capital with faster
low and mid rates of technological productivity
growth
-0.02
-0.08
high
-0.14
-0.2
1900 1920 1940 1960 1980 2000 2020 2040
Time (Year) Simulation results using
Marginal Productivity of Capital : flin04 the plausible trajectories of
Marginal Productivity of Capital : flin03 technical efficiency growth
Marginal Productivity of Capital : flin02 as a function of cumulative
Marginal Productivity of Capital : flin01
Marginal Productivity of Capital : Empirical Data primary exergy production
28/04/2003 16
17. Marginal productivity of labour
Graph for Marginal Productivity of Labour
0.6 Slower decrease in the marginal
productivity of labour with faster
rate of technological productivity
growth
0.45
0.3
0.15
0
1900 1920 1940 1960 1980 2000 2020 2040
Time (Year) Simulation results using
the plausible trajectories of
Marginal Productivity of Labour : flin04
Marginal Productivity of Labour : flin03 technical efficiency growth
Marginal Productivity of Labour : flin02 as a function of cumulative
Marginal Productivity of Labour : flin01 primary exergy production
Marginal Productivity of Labour : Empirical Data
28/04/2003 17
18. US “Dematerialisation” forecasts #1
dmat_sens
Empirical Data Empirical Data
25% 50% 75% 95% 25% 50% 75% 95%
Primary Exergy Intensity of Output Gross Output
2 60
Exergy Intensity of Output Forecast GDP (1900 = 1)
(index 1900 = 1) for sensitivity tests varying
the "dematerialisation rate"
from 1.2 to 1.5% per annum
1.5 45
1 30
0.5 15
0 0
1900 1938 1975 2013 2050 1900 1938 1975 2013 2050
Time (Year) Time (Year)
28/04/2003 18
19. US “Dematerialisation” forecasts #2
Empirical Data Empirical Data
25% 50% 75% 95% 25% 50% 75% 95%
Technical Efficiency of Primary Exergy Conversion Primary Exergy Demand
0.4 8
Technical Efficiency Primary Exergy Demand
for sensitivity tests varying (index, 1900=1)
the "dematerialisation rate"
0.3 6
from 1.2 to 1.5% per annum
0.2 4
0.1 2
0 0
1900 1938 1975 2013 2050 1900 1938 1975 2013 2050
Time (Year) Time (Year)
28/04/2003 19
20. US “Dematerialisation” forecasts #3
dmat_sens
Empirical Data Empirical Data
25% 50% 75% 95% 25% 50% 75% 95%
Marginal Productivity of Labour Marginal Productivity of Capital
0.6 0.04
0.45 -0.02
0.3 -0.08
0.15 -0.14
MP of Labour MP of Capital
0 -0.2
1900 1938 1975 2013 2050 1900 1938 1975 2013 2050
Time (Year) Time (Year)
dmat_sens
Empirical Data
"Dematerialisation" Sensitivity 25% 50% 75% 95%
Analysis Marginal Productivity of Exergy Services
1
MP of Exergy Services
Varying the "exergy intensity of output"
0.9
reduction rate between 1.2 and 1.5%,
using an exponential distribution 0.8
(order: 0, stretch: 0.015), using the
parameters for the high trajectory of 0.7
technical efficiency growth.
0.6
1900 1938 1975 2013 2050
Time (Year)
28/04/2003 20
21. Exergy Service Breakdown Comparison of Japan and US
F r a c tio n s o f fo s s il fu e l e x e r g y a p p a r e n t c o n s u m p tio n ,
Japan 1900-2000
100% E le c tric ity
r e m o v e la y e r s t h e n a d d P r im e m o v e r s
th e s e o n N o n -fu e l
90% s u c c e s s ively Heat
H e a t (U S )
E le c tric ity (U S )
80% P r im e m o v e r s ( U S )
N o n -fu e l (U S )
70%
60%
50%
40%
30%
20%
10%
28/04/2003 0% 21
22. REXS-F: Japan Forecasts #1
Forecast Technical Efficiency REXS F US100
0.4
0.2
0
1960 1978 1996 2014 2032 2050
year
empirical
v1
v2
v3
28/04/2003 22
23. REXS-F: Japan Forecasts #2
Forecast Gross Output REXS F US100
15
7.5
0
1960 1978 1996 2014 2032 2050
year
empirical
v2
v3
Gross Output : v3
28/04/2003 23
24. REXS-F: Japan Forecasts #3
MP of labour (V2 - Slow technical efficiency growth)
1
0.5
0
1960 1975 1990 2005 2020 2035 2050
year
MP of labour (V3 - Fast technical efficiency growth)
1
0.5
0
1960 1975 1990 2005 2020 2035 2050
year
Labour
28/04/2003 Capital 24
Exergy Services