Organisation for Economic Co-operation and Development. 2006. Infrastructure to 2030. Paris: OECD.
”Tomorrow’s infrastructure will demand a much lower spend than today’s fixed line and cellular mobile and will be much quicker to roll out” (53).
“Often overestimated in the past, telecommunications will nevertheless increasingly be viewed as a potential substitute for travel. This could reduce dependencies on vehicles and their fuels and ameliorate the effects of pollution” (54).
“This section briefly reviews the telecommunication infrastructure developments of the past 25 years by examining gfour areas of trends in infrastructure investment:
-past demand and the main drivers
-current state of the infrastructure
-whether past developments in demand will continue
-which trends seem clearly unsustainable” (61).
The publication presents a table over viewing the main drivers of telecommunication demand. Under the “main drivers” of broadband (long-distance, not local loop) demand are “cost and technical (ease of upgrading capacity) for growing international data traffic, especially Internet users after 1996/7 as World Wide Web interface made the Internet popular” (62). It is clear that the roll-out of broadband is very heavily driven by consumer demand.
“The major point here is that there is an underlying force behind the successful developments of recent times. Liberalization has been a significant driver of infrastructure development over the past 25 years since it has allowed the door to open to a new era of multiple infrastructures. Demand-side forces, especially the power of consumers acting on the political balance in government, have produced a revolution in the three key infrastructure drivers of telecommunications:
-…deregulation…
-competition of all kinds…
-new technology…” (64).
What about the future of telecom demand? “With the growing importance of the knowledge-based economy, the demand for telecommunications and its infrastructure will continue to expand well beyond 2030” (68).
“Key factors driving future demand and infrastructure investment” (70).
“-geopolitics;
-security;
-macroeconomy;
-finance;
-demography;
-environment;
-technology;
-governance;
-telecommunications pricing;
-microeconomics—primary consumer basics inflation” (70).
“Projected trends in demand for telecoms and investment to 2030” (78).
“Accessibility = control of access + cost of access + ease of access + ubiquity + speed of rollout” (79).
“In general we can say that the main trend in demand for telecommunications that will drive infrastructure is a massive expansion of demand globally from developin countries, firstly as incomes increase to around the USD 3 000 level. Secondly we can expect take-up for those below this income level, as prices fall” (82).
“In later years, beyond 2015, an increasing component of traffic is expected to be machine-to-machine communications and transactions over the Internet, especially in industrial applications such as supply chain, process control and also in financial transactions for person to machine. An ‘Internet of things’ will grow up. Machine-to-machine communications for industrial and consumer functions and person-to-machine, for instance to make purchases, will be increasingly an everyday event. The impact on infrastructure will be to increase not only the traffic, but the address space required for all the new respondents” (84).
“The telecommunications industry has been notoriously poor at forecasting the take-up of new technologies and services, largely because market forecasts have often been supply driven and technocentric and do not focus on understanding consumers; behavior and their needs—and what they will pay for…A better approach to forecasting is to identify and understand the underlying trends and their drivers, and to take a demand-driven approach. Firstly a more global view of the market is not essential. This perspective provides an understanding that the overall telecommunications market has the potential to grow to three or four times its current size in terms of the number of consumers, by expansion outside the OECD group. This pricing is important and this will be the key stimulant for consumer take-up. A second key finding is that ‘content is not king’; instead it is connectivity that consumers will pay for as their primary m0otivation as this new market opens…Third, wireless technologies are developing rapidly and allowing infrastructure to be rolled out more cost effectively than ever before, offering the promise of connectivity anytime, anywhere, and at even lower prices…” (85).
The issue of infrastructure investment is then explored. It is highlighted that telecom investment, as a percentage of total infrastructure investment, has found its share steadily growing (86).
Private v public investment: “Private sector-led growth in infrastructure investment has revolutionzed access to telecommunications services around the world over the past ten years. For the OECD community we see that deregulation has allowed new pricing regimes, not controlled by the incumbents, and specifically the VoIP market to take off, despite attempts to ban or restrict it in some countries” (88).
“The key growth regions for telecommunications infrastructure, as outlined above, are in developing countries” (88).
There is a discussion of the future mix of technology, and how this relates to current infrastructure constraints (the local loop, for example).
“In consequence it is possible to see the progressive arrival of a new model of infrastructure, heavily influenced by the requirements of the low-cost demands, from developing countries seeking more ‘bits per buck’ for their billions of people in the under USD 3 000 per year income bracket” (101).
“The result may be parallel operation of two infrastructure models globally for at least a decade: a previous OECD model of mixed long distance carrier standards for trunk networking with a legacy of wireline in broadband DSL and CATV cable in the local loop, and at the same time, the simpler two level radio access/fibre long distance model slowly spreading across the world” (103). The mix of this parallel development is explored for different key regions on 110-1.
Cross-infrastructure impacts on transport are explored on 122-5. On health and medicine on 128-9. On the elderly on 130-1. On education on 131-3. On crime on 133-4.
The chapter ends with policy recommendations.
Showing posts with label ICT. Show all posts
Showing posts with label ICT. Show all posts
Wednesday, January 13, 2010
Monday, January 11, 2010
Stordahl: Long-Term Broadband Technology Forecasting
Stordahl, K. 2004. “Long-term broadband technology forecasting.” Teletronikk 4(4.2004): 13-31.
“This paper gives an overview of the relevant broadband technologies, describes their market positions and possibilities. Diffusion models are used to make long-term broadband forecasts for the Western European residential market. The forecasts are separated for the main broadband technologies…based on market share predictions for each technology. The long-term forecasts are evaluated and compared with other broadband forecasts for the Western Europe market” (13).
There is increasing difficulty to make specific, long-term forecasts of ICT related technologies because
the market has become deregulated.
In order to understand “broadband rollout”, we must have two components: we must first know the overall broadband coverage, and secondly the “take rate”, which is the, “proportion between demand and coverage in an area” (17).
“The long-term broadband forecasts are based on results from techno-economic calculations. The techno-economic calculations evaluate the ‘economic value’, i.e. expressed by net present value or pay back [sic] period of rollout of different broadband technologies. The assessments have been carried out for rollout on a national level and on specific areas like urban, suburban, rural and especially the rest market to examine the potential of the different broadband technologies. The main results from the techno-economic calculations show that it is difficult for new broadband technologies to capture significant market share in areas where cable modem and/or DSL are already deployed” (18).
“Important drivers in the broadband market are of course the applications. The evolution of applications generates continuously higher broadband penetration” (21). Other important drivers are the online content and the government supported programs promoting the technology.
“The broadband forecasts for the different technologies are modeled by beginning with the broadband penetration forecasts developed for the total broadband demand in the Western European residential market. Based on experience from the last few years, diffusion type models have proved to have the best abilities for long-term forecast modeling” (22).
There is a broadband forecast from 2000-2010 for Western Europe, with the inflection point at around 2005-6.
The author then compares broadband forecasts for 7 models.
“This paper gives an overview of the relevant broadband technologies, describes their market positions and possibilities. Diffusion models are used to make long-term broadband forecasts for the Western European residential market. The forecasts are separated for the main broadband technologies…based on market share predictions for each technology. The long-term forecasts are evaluated and compared with other broadband forecasts for the Western Europe market” (13).
There is increasing difficulty to make specific, long-term forecasts of ICT related technologies because
the market has become deregulated.
In order to understand “broadband rollout”, we must have two components: we must first know the overall broadband coverage, and secondly the “take rate”, which is the, “proportion between demand and coverage in an area” (17).
“The long-term broadband forecasts are based on results from techno-economic calculations. The techno-economic calculations evaluate the ‘economic value’, i.e. expressed by net present value or pay back [sic] period of rollout of different broadband technologies. The assessments have been carried out for rollout on a national level and on specific areas like urban, suburban, rural and especially the rest market to examine the potential of the different broadband technologies. The main results from the techno-economic calculations show that it is difficult for new broadband technologies to capture significant market share in areas where cable modem and/or DSL are already deployed” (18).
“Important drivers in the broadband market are of course the applications. The evolution of applications generates continuously higher broadband penetration” (21). Other important drivers are the online content and the government supported programs promoting the technology.
“The broadband forecasts for the different technologies are modeled by beginning with the broadband penetration forecasts developed for the total broadband demand in the Western European residential market. Based on experience from the last few years, diffusion type models have proved to have the best abilities for long-term forecast modeling” (22).
There is a broadband forecast from 2000-2010 for Western Europe, with the inflection point at around 2005-6.
The author then compares broadband forecasts for 7 models.
Fildes and Kumar: Telecommunications Demand Forecasting - A Review
Fildes, Robert, and V. Kumar. “Telecommunications demand forecasting--a review.” International Journal of Forecasting 18(4) (October): 489-522. doi:10.1016/S0169-2070(02)00064-X.
“This paper describes various models that have been used to understand market dynamics. Markets discussed include both established and new: mobile, the internet, and PSTN… Cross-sectional choice models of the mode of accessing the service are discussed along with models for usage in established markets. These models typically include price…differ4entials and use standard econometric methods, focusing on price elasticity estimation. Forecasting accuracy has been neglected. New product models may include additional ‘drivers’ such as aspects of service quality and the attributes of the products themselves. Both choice models of adoption of new products and Bass-type diffusion models have been used in forecasting. Because of the complexity of the ‘drivers’ of the adoption process, the successful modeling of these markets has been limited, not least by inadequate data. Simulation models have been proposed to structure the problems more completely and overcome these inadequacies. Both these classes of model have not been effectively validated, researchers having been content to just propose a new approach without thoroughly testing it against alternatives” (Robert Fildes and V. Kumar, 1).
“The core concept at the heart of most models of telecommunications demand is simple—that expected usage determines access demand and these two in turn determine equipment requirements” (R Fildes and V Kumar 2002).
“In standard models of demand in the PSTN, modeling is though a two stage procedure where usage (quantity in minutes demanded or number of calls) is modeled as a function of price, income and other relevant variables conditional on access while access is modeled through a model of the individual choices made between alternatives. here the dependent variable is categorical and the logit framework, which we discuss later in this section, offers a suitable representation” (R Fildes and V Kumar 2002, 5).
“This paper describes various models that have been used to understand market dynamics. Markets discussed include both established and new: mobile, the internet, and PSTN… Cross-sectional choice models of the mode of accessing the service are discussed along with models for usage in established markets. These models typically include price…differ4entials and use standard econometric methods, focusing on price elasticity estimation. Forecasting accuracy has been neglected. New product models may include additional ‘drivers’ such as aspects of service quality and the attributes of the products themselves. Both choice models of adoption of new products and Bass-type diffusion models have been used in forecasting. Because of the complexity of the ‘drivers’ of the adoption process, the successful modeling of these markets has been limited, not least by inadequate data. Simulation models have been proposed to structure the problems more completely and overcome these inadequacies. Both these classes of model have not been effectively validated, researchers having been content to just propose a new approach without thoroughly testing it against alternatives” (Robert Fildes and V. Kumar, 1).
“The core concept at the heart of most models of telecommunications demand is simple—that expected usage determines access demand and these two in turn determine equipment requirements” (R Fildes and V Kumar 2002).
“In standard models of demand in the PSTN, modeling is though a two stage procedure where usage (quantity in minutes demanded or number of calls) is modeled as a function of price, income and other relevant variables conditional on access while access is modeled through a model of the individual choices made between alternatives. here the dependent variable is categorical and the logit framework, which we discuss later in this section, offers a suitable representation” (R Fildes and V Kumar 2002, 5).
Wednesday, August 19, 2009
The Economic Benefits of Intelligent Technologies
Access Economics Pty Limited. 2009. The Economic Benefits of Intelligent Technologies. April.
"This report reviews the potential economic benefits that may flow from the adoption of smart technologies and systems in different parts of the economy...The amount of all forms of data that are regularly collected throughout the community rapidly expand on a daily basis. Intelligent systems have the capacity of using these data to improve decision-making and co-ordination throughout society, thereby lifting economic efficiency and living standards" (i).
"Overall, the adoption of smart technologies and systems in the five areas identified are conservatively estimated to result in:
• an increase in the net present value (NPV) of Gross Domestic Product...of between $35 billion to $80 billion over the first ten years, with precise estimates depending on how much spare capacity is in the economy;
• in the case where the economy is operating at full employment, an increase of labour productivity of around 0.5% as the deployment of the technologies becomes widespread; and
• in the case where the economy is operating at less than full employment the impact on jobs is more pronounced as the technologies become widespread. In 2014 alone this results in more than 70,000 jobs being added to the economy" (ii).
This report explores different areas in which smart technology can have profound effects, specifically electricity, transport, health, water and high-speed broadband (HSBB).
"The rollout of high speed broadband both enables the more effective implementation of smart technologies in other areas of the economy-including electricity, water, transport and health-as well as providing other direct and substantive benefits to individuals, businesses and the environment through the availability to deliver a range of commercial, government and information services more efficiently" (1).
The report reviews the literature on the topic, and then explores smart-technologies in an equilibrium model. Two scenarios are then explored: an economy at full employment and an economy with less than full employment.
"This report reviews the potential economic benefits that may flow from the adoption of smart technologies and systems in different parts of the economy...The amount of all forms of data that are regularly collected throughout the community rapidly expand on a daily basis. Intelligent systems have the capacity of using these data to improve decision-making and co-ordination throughout society, thereby lifting economic efficiency and living standards" (i).
"Overall, the adoption of smart technologies and systems in the five areas identified are conservatively estimated to result in:
• an increase in the net present value (NPV) of Gross Domestic Product...of between $35 billion to $80 billion over the first ten years, with precise estimates depending on how much spare capacity is in the economy;
• in the case where the economy is operating at full employment, an increase of labour productivity of around 0.5% as the deployment of the technologies becomes widespread; and
• in the case where the economy is operating at less than full employment the impact on jobs is more pronounced as the technologies become widespread. In 2014 alone this results in more than 70,000 jobs being added to the economy" (ii).
This report explores different areas in which smart technology can have profound effects, specifically electricity, transport, health, water and high-speed broadband (HSBB).
"The rollout of high speed broadband both enables the more effective implementation of smart technologies in other areas of the economy-including electricity, water, transport and health-as well as providing other direct and substantive benefits to individuals, businesses and the environment through the availability to deliver a range of commercial, government and information services more efficiently" (1).
The report reviews the literature on the topic, and then explores smart-technologies in an equilibrium model. Two scenarios are then explored: an economy at full employment and an economy with less than full employment.
Tuesday, August 4, 2009
Transforming America's Power Industry
The Brattle Group. Transforming America's Power Industry: The Investment Challenge 2010-2030.
The AEO makes projections out to 2030 of US electricity consumption by type of production. The Brattle Group, through the use of the Regional Capacity Model (RECAP), then took these projections, makes its own projections out to 2030 and develops scenarios around uncertainty.
In the Brattle Group's Reference Scenario, one important initial assumption that was changed was the overall price of construction for different types of energy. These revised numbers came from an updated EPRI report that highlighted the increasing cost of construction above inflation, which was the assumption of the AEO 2008 report.
The Brattle Group also used higher Fuel Prices than AEO in their forecast.
The Reference Scenario is similar to the AEO figure, but incorporates higher fuel and construction costs (outlined in more detail in the report). "The Reference Scenario should not be viewed as our 'base' or 'most likely' scenario, but rather a starting point for our analysis" (11).
In addition to the Reference Scenario, the Brattle Group produced scenarios that focused on energy savings provided by EE/DR (Energy Efficiency/Demand Response) measures. These scenarios were broken down two-fold: firstly, there was assumed to be "realistically achievable potential" (RAP). Secondly, there was assumed to be "maximum achievable potential" (MAP). "...our RAP Efficiency Base Case Scenario includes EE/DR savings as our best estimate of projected demand for electricity prior to the full modeling of price response or a national carbon policy" (16).
"One of the two components of the EPRI forecast is energy efficiency (EE). The EE forecasts consider an extensive set of technologies and measures for the residential, commercial, and industrial sectors. These EE technologies and measures affect different end uses. Programs, products, and services that encourage customers to adopt EE technologies and measures come in several forms, including rebates and subsidies. Following are some of the various technologies and measures considered in teh EPRI study and the end uses they affect" (16).
1. Residential High-Efficiency Equipment: HVAC, lighting, etc.
2. Commercial High-Efficiency Equipment: Same as above for commercial buildings.
3. Industrial High-Efficiency Equipment: Further along the same lines as above, but for industry.
Demand Response, on the other hand, is focused heavily on reducing peak demand for energy. Three types of demand response were modeled:
1. Direct Load Control (DLC): utility companies can shut off an end-user's A/C, for example.
2. Interruptible Service: utility companies can ask industry or commercial groups to reduce energy at certain times.
3. Dynamic Pricing: This requires AMI infrastructure.
"A major cost that is likely to be capitalized in the EE/DR forecast is investment in AMI, the equipment that enables dynamic pricing (as well as a wide range of operational benefits and reliability improvements). Harvesting potential gains from DR programs will require a substantial capital investment in AMI, as well as consumption patterns away from peak periods in response to price signals. To estimate the capital cost of DR initiatives, we separately projected the investment in AMI that likely would be necessary to support these forecasts. Our projection of AMI investment costs is driven primarily by three factors:" (22-3).
1. Final AMI Penetration Rate: "For the MAP Efficiency Scenario, we have assumed that 30 percent of residential customers and 50 percent of C&I customers would be equipped with AMI. These participation rates were reduced by roughly 60 percent to produce the RAP Efficiency Base Case Scenario" (23).
2. AMI Deployment Rate Over Time: "We assume that AMI deployment will begin in 2010 for C&I customers and in 2015 for residential customers. Full deployment will be reached in 2030 for the RAP Efficiency Base Case Scenario. Deployment is accelerated under the MAP Efficiency Scenario, reaching full deployment in 2020" (23).
3. Cost of AMI per Customer: "Based on a review of California shareholder filings for AMI budget approval, we have estimated the full cost per residential customer to be $300. The cost per C&I customer is estimated at $1,500" (23).
The fourth scenario developed explored the implications of the "Prism Analysis" conducted by EPRI. This focuses on the different feasible areas in which the electricity sector can reduce carbon emissions. The Prism Analysis is comprised of seven events:
1. Energy Efficiency Reduction in Load Growth
2. Doubling the level of Renewable Generation Capacity Over AEO 2008 Forecast
3. Tripling Nuclear Capacity over AEO 2008.
4. Improve Efficiency of Coal Generation
5. CCS
6. PHEVs
7. Increased Distributed Energy Resources (DER)
Four major technologies comprise the core of these seven events:
1. Energy Effiency: The assumptions in the Prism RAP Scenario for EE/DR were the same as in the RAP Efficiency Base Case Scenario
2. CCS: All factories after 2020 were assumed to have CCS with 90% efficacy
3. Renewables: These increased assuming more and expanding RPS requirements
4. Nuclear
The AEO makes projections out to 2030 of US electricity consumption by type of production. The Brattle Group, through the use of the Regional Capacity Model (RECAP), then took these projections, makes its own projections out to 2030 and develops scenarios around uncertainty.
In the Brattle Group's Reference Scenario, one important initial assumption that was changed was the overall price of construction for different types of energy. These revised numbers came from an updated EPRI report that highlighted the increasing cost of construction above inflation, which was the assumption of the AEO 2008 report.
The Brattle Group also used higher Fuel Prices than AEO in their forecast.
The Reference Scenario is similar to the AEO figure, but incorporates higher fuel and construction costs (outlined in more detail in the report). "The Reference Scenario should not be viewed as our 'base' or 'most likely' scenario, but rather a starting point for our analysis" (11).
In addition to the Reference Scenario, the Brattle Group produced scenarios that focused on energy savings provided by EE/DR (Energy Efficiency/Demand Response) measures. These scenarios were broken down two-fold: firstly, there was assumed to be "realistically achievable potential" (RAP). Secondly, there was assumed to be "maximum achievable potential" (MAP). "...our RAP Efficiency Base Case Scenario includes EE/DR savings as our best estimate of projected demand for electricity prior to the full modeling of price response or a national carbon policy" (16).
"One of the two components of the EPRI forecast is energy efficiency (EE). The EE forecasts consider an extensive set of technologies and measures for the residential, commercial, and industrial sectors. These EE technologies and measures affect different end uses. Programs, products, and services that encourage customers to adopt EE technologies and measures come in several forms, including rebates and subsidies. Following are some of the various technologies and measures considered in teh EPRI study and the end uses they affect" (16).
1. Residential High-Efficiency Equipment: HVAC, lighting, etc.
2. Commercial High-Efficiency Equipment: Same as above for commercial buildings.
3. Industrial High-Efficiency Equipment: Further along the same lines as above, but for industry.
Demand Response, on the other hand, is focused heavily on reducing peak demand for energy. Three types of demand response were modeled:
1. Direct Load Control (DLC): utility companies can shut off an end-user's A/C, for example.
2. Interruptible Service: utility companies can ask industry or commercial groups to reduce energy at certain times.
3. Dynamic Pricing: This requires AMI infrastructure.
"A major cost that is likely to be capitalized in the EE/DR forecast is investment in AMI, the equipment that enables dynamic pricing (as well as a wide range of operational benefits and reliability improvements). Harvesting potential gains from DR programs will require a substantial capital investment in AMI, as well as consumption patterns away from peak periods in response to price signals. To estimate the capital cost of DR initiatives, we separately projected the investment in AMI that likely would be necessary to support these forecasts. Our projection of AMI investment costs is driven primarily by three factors:" (22-3).
1. Final AMI Penetration Rate: "For the MAP Efficiency Scenario, we have assumed that 30 percent of residential customers and 50 percent of C&I customers would be equipped with AMI. These participation rates were reduced by roughly 60 percent to produce the RAP Efficiency Base Case Scenario" (23).
2. AMI Deployment Rate Over Time: "We assume that AMI deployment will begin in 2010 for C&I customers and in 2015 for residential customers. Full deployment will be reached in 2030 for the RAP Efficiency Base Case Scenario. Deployment is accelerated under the MAP Efficiency Scenario, reaching full deployment in 2020" (23).
3. Cost of AMI per Customer: "Based on a review of California shareholder filings for AMI budget approval, we have estimated the full cost per residential customer to be $300. The cost per C&I customer is estimated at $1,500" (23).
The fourth scenario developed explored the implications of the "Prism Analysis" conducted by EPRI. This focuses on the different feasible areas in which the electricity sector can reduce carbon emissions. The Prism Analysis is comprised of seven events:
1. Energy Efficiency Reduction in Load Growth
2. Doubling the level of Renewable Generation Capacity Over AEO 2008 Forecast
3. Tripling Nuclear Capacity over AEO 2008.
4. Improve Efficiency of Coal Generation
5. CCS
6. PHEVs
7. Increased Distributed Energy Resources (DER)
Four major technologies comprise the core of these seven events:
1. Energy Effiency: The assumptions in the Prism RAP Scenario for EE/DR were the same as in the RAP Efficiency Base Case Scenario
2. CCS: All factories after 2020 were assumed to have CCS with 90% efficacy
3. Renewables: These increased assuming more and expanding RPS requirements
4. Nuclear
Friday, July 31, 2009
Hledik: How Green Is the Smart Grid?
Hledik, R. 2009. How Green Is the Smart Grid? The Electricity Journal 22, no. 3: 29-41.
"A simulation of the US power system suggests that both conservative and more technologically aggressive implementations of a smart grid would produce a significant reduction in power sector carbon emissions at the national level. A conservative approach could reduce annual CO2 emissions by 5 percent by 2030, while the more aggressive approach could lead to a reduction of nearly 16 percent by 2030" (29).
Two scenarios are examined, as the concept of "smart grid" has not been fully concretized. Scenario one explores technologies that are currently available. The second explores an expansion of possible future technologies.
"At a basic level, the smart grid will serve as the information technology backbone that enables widespread penetration of new technologies that today's electrical grid cannot support. These new technologies include cutting-edge advancements in metering, transmission, distribution, and electricity storage technology, as well as providing new information and flexibility to both consumers and providers of electricity. Ultimately, access to this information will improve the products and services that are offered to consumers, leading to more efficient consumption and provision of electricity" (30).
A key aspect of smart grid technology is advanced metering infrastructure (AMI). This leads to dynamic pricing of energy.
There are a variety of pushes to produce more energy from renewable sources. These each have different goals. Doubling the Renewable Portfolio Standards (RPS) would lead to 19% of energy being produced by renewables in 2030. T. Boone Pickens calls for 20% from renewable by 2020, as does the EU. The New Apollo program wants 25% by 2025. Google's energy plan calls for 60% by 2030. Repower America suggests that 75% come from renewables in the next 10 years.
The Brattle Group's RECAP model is used to explore these scenarios using EIA data and assumptions.
"Among the key outputs of RECAP is a forecast of the CO2 emissions from both existing and new power plants. This forecast depends on both the projected mix of new plants that will be added to the system, and the operation of all power plants that are connected to the grid. Implementation of a smart grid will influence both of these. In this study, there are four specific smart grid impacts that have been modeled" (37). These are: peak demand reduction, conservation, increased penetration of renewables and reduced line loss.
Three scenarios are explored out to 2030. Firstly, a business as usual forecast, which shows average annual increases in CO2 emissions of 0.7%. Secondly, the Conservative Scenario, which posits the effects of existing ICT technology on CO2 shows an average annual growth of 0.5%. Finally, the Expanded Scenario shows an average annual growth rate of -0.1%.
Overall, dynamic pricing leads to a nominal improvement in overall CO2 emissions. The combination of dynamic pricing and information displays leads to a 5% reduction in annual CO2 emissions. "By far, the single largest reduction comes from the cleaner mix of generating capacity that is enabled by distributed resources and an expanded transmission system, amounting to a 9.9% reduction in CO2 emissions" (39).
"A simulation of the US power system suggests that both conservative and more technologically aggressive implementations of a smart grid would produce a significant reduction in power sector carbon emissions at the national level. A conservative approach could reduce annual CO2 emissions by 5 percent by 2030, while the more aggressive approach could lead to a reduction of nearly 16 percent by 2030" (29).
Two scenarios are examined, as the concept of "smart grid" has not been fully concretized. Scenario one explores technologies that are currently available. The second explores an expansion of possible future technologies.
"At a basic level, the smart grid will serve as the information technology backbone that enables widespread penetration of new technologies that today's electrical grid cannot support. These new technologies include cutting-edge advancements in metering, transmission, distribution, and electricity storage technology, as well as providing new information and flexibility to both consumers and providers of electricity. Ultimately, access to this information will improve the products and services that are offered to consumers, leading to more efficient consumption and provision of electricity" (30).
A key aspect of smart grid technology is advanced metering infrastructure (AMI). This leads to dynamic pricing of energy.
There are a variety of pushes to produce more energy from renewable sources. These each have different goals. Doubling the Renewable Portfolio Standards (RPS) would lead to 19% of energy being produced by renewables in 2030. T. Boone Pickens calls for 20% from renewable by 2020, as does the EU. The New Apollo program wants 25% by 2025. Google's energy plan calls for 60% by 2030. Repower America suggests that 75% come from renewables in the next 10 years.
The Brattle Group's RECAP model is used to explore these scenarios using EIA data and assumptions.
"Among the key outputs of RECAP is a forecast of the CO2 emissions from both existing and new power plants. This forecast depends on both the projected mix of new plants that will be added to the system, and the operation of all power plants that are connected to the grid. Implementation of a smart grid will influence both of these. In this study, there are four specific smart grid impacts that have been modeled" (37). These are: peak demand reduction, conservation, increased penetration of renewables and reduced line loss.
Three scenarios are explored out to 2030. Firstly, a business as usual forecast, which shows average annual increases in CO2 emissions of 0.7%. Secondly, the Conservative Scenario, which posits the effects of existing ICT technology on CO2 shows an average annual growth of 0.5%. Finally, the Expanded Scenario shows an average annual growth rate of -0.1%.
Overall, dynamic pricing leads to a nominal improvement in overall CO2 emissions. The combination of dynamic pricing and information displays leads to a 5% reduction in annual CO2 emissions. "By far, the single largest reduction comes from the cleaner mix of generating capacity that is enabled by distributed resources and an expanded transmission system, amounting to a 9.9% reduction in CO2 emissions" (39).
Thursday, February 26, 2009
Romm: The Internet and the New Energy Economy
Romm, J. 2002. The Internet and the New Energy Economy. Sustainability at the Speed of Light. Sweden, WWF.
In the period from 1996 to 1999, there was a drop of 3.2% per year in energy intensity, which is a measure of the amount of energy required to produce one output of GDP.
"Growth in the Internet Economy can cut energy intensity in two ways. First, the IT sector is less energy-intensive than traditional manufacturing, so growth in this sector engenders less incremental energy consumption. Second, the Internet Economy appears to be increasing efficiency in every sector of the economy, which is the primary focus of this paper" (131).
"In the late 1990s, a startling shift appeared in the statistics. The nation's energy intensity dropped 3.7% in 1997 and 3.9% in 1998. It is unprecedented for the US economy to see such improvements in energy intensity during a period of low energy prices and relatively low public awareness of energy issues" (133).
"Analysis by EPA and the Argonne National Laboratory suggests that one third to one half of the recent improvements in energy intensity are 'structural.' Structural gains occur when economic growth shifts to sectors of the economy that are not particularly energy intensive--such as the IT sector, including computer manufacturing and software--as opposed to more energy-intensive sectors, including chemicals, pulp and paper industry, and construction" (135).
Thus, one third to one half of the 3.2% average decline from 96-99 are structural, of which the internet economy represents an ambiguous portion.
"The Internet does not consume 8% of US electricity as Mills claims. The Koomey et al. analysis showed that this estimate is too large by a factor of eight. Computers, office equipment, and the like do not consume 13% of electricity, as Mills claim; a better number is 3%" (146).
In the period from 1996 to 1999, there was a drop of 3.2% per year in energy intensity, which is a measure of the amount of energy required to produce one output of GDP.
"Growth in the Internet Economy can cut energy intensity in two ways. First, the IT sector is less energy-intensive than traditional manufacturing, so growth in this sector engenders less incremental energy consumption. Second, the Internet Economy appears to be increasing efficiency in every sector of the economy, which is the primary focus of this paper" (131).
"In the late 1990s, a startling shift appeared in the statistics. The nation's energy intensity dropped 3.7% in 1997 and 3.9% in 1998. It is unprecedented for the US economy to see such improvements in energy intensity during a period of low energy prices and relatively low public awareness of energy issues" (133).
"Analysis by EPA and the Argonne National Laboratory suggests that one third to one half of the recent improvements in energy intensity are 'structural.' Structural gains occur when economic growth shifts to sectors of the economy that are not particularly energy intensive--such as the IT sector, including computer manufacturing and software--as opposed to more energy-intensive sectors, including chemicals, pulp and paper industry, and construction" (135).
Thus, one third to one half of the 3.2% average decline from 96-99 are structural, of which the internet economy represents an ambiguous portion.
"The Internet does not consume 8% of US electricity as Mills claims. The Koomey et al. analysis showed that this estimate is too large by a factor of eight. Computers, office equipment, and the like do not consume 13% of electricity, as Mills claim; a better number is 3%" (146).
Laitner: Information Technology and US Energy Consumption
Laitner, JAHS. 2002. Information Technology and US Energy Consumption: Energy Hog, Productivity Tool, or Both? Journal of Industrial Ecology 6, no. 2: 13-24.
The authors suggest that the wrong questions are being asked in the debate about ICT and energy consumption. Current questions are too limited. Instead, we should be asking questions like this: "What impact with the information age have on our ability to produce goods and services within our economy; and what impact will it have, in turn, on the nation's overall energy requirements? IN shot, will the information economy prove to be an energy hog, a productivity tool or both?" (14).
The author argues that yes, ICT does have an impact on energy efficiency. "Even with a small correction to reflect the influence of weather, it appears that the annual rate of change in 1996-2001 was surprisingly larger than many analysts might have expected on the basis of past trends. This is all the more surprising because it occurred in the absence of any significant price signals or major energy policy initiatives within the United States" (15).
"The initial evidence with respect to information technologies appears to support a trend toward decreasing energy intensity compared to the trends now represented in conventional forecasts. Nonetheless, as long as GDP grows faster than the decline in energy intensity, overall energy consumption will continue to increase, albeit at a smaller rate" (20).
The authors suggest that the wrong questions are being asked in the debate about ICT and energy consumption. Current questions are too limited. Instead, we should be asking questions like this: "What impact with the information age have on our ability to produce goods and services within our economy; and what impact will it have, in turn, on the nation's overall energy requirements? IN shot, will the information economy prove to be an energy hog, a productivity tool or both?" (14).
The author argues that yes, ICT does have an impact on energy efficiency. "Even with a small correction to reflect the influence of weather, it appears that the annual rate of change in 1996-2001 was surprisingly larger than many analysts might have expected on the basis of past trends. This is all the more surprising because it occurred in the absence of any significant price signals or major energy policy initiatives within the United States" (15).
"The initial evidence with respect to information technologies appears to support a trend toward decreasing energy intensity compared to the trends now represented in conventional forecasts. Nonetheless, as long as GDP grows faster than the decline in energy intensity, overall energy consumption will continue to increase, albeit at a smaller rate" (20).
Laitner and Ehrhardt-Martinez: Information and Communication Technologies: The Power of Productivity
Laitner, John, and Karen Ehrhardt-Martinez. 2008. Information and Communication Technologies: The Power of Productivity. American Council for an Energy-Efficient Economy, February.
ICT helps to promote energy consumption savings. "For every extra kilowatt-hour of electricity that has been demanded by ICT, the US economy increased its overall energy savings by a factor of about 10" (v). There has been a delinking of economic growth from growth in energy consumption, and this report posits that ICT is a major contributor to this delinking. In addition, it indicates that we are probably not using ICT to its fullest potential.
"There is broad agreement that greater levels of productivity can lead to greater economic returns" (1).
"During the current historical period, gains in productivity are most likely to result from the continued development and application of new information and communications technologies" (1).
There is an outline of how ICT contributes to productivity, relying heavily on Jorgenson et al (2005). Then the topic of energy is explored: "...it does appear that ICT investments may actually be 'energy saving' more broadly speaking. That is, the same digital age investments that are driving a more robust economic productivity are also increasing the efficiency in how we use energy more generally" (3)
"As we implied in the previous section, ICT has not only transformed our economy and our lives, they ahve also reinvigorated economic productivity. What is less well-recognized is that ICT systems have revolutionized the relationship between economic production and energy consumption" (4).
"To more fully explore the ICT paradox and to gain a better understanding of the potential net energy benefits provided by ICT, this report explores the following questions: What is the enabling role of ICT investment and how might they be expanded to increase energy productivity beyond current patterns of improvement? How might a productivity-led ICT strategy provide greater energy security while contributing toward climate change mitigation efforts? What do current energy and efficiency trends look like and how do ICT provide a positive complement within the emerging trends? We begin by assessing where ICT fit within the historical and technological context and by discussing our working definition of the term 'energy efficiency" (4).
"Energy efficiency is a process that achieves the same ends with fewer energy inputs. It's about producing, transporting, traveling, lighting, cooking, heating, and communicating in ways that maintain or increase our productivity for every unit of energy consumed. In other words, energy efficiency is about providing the same goods and services using less energy. Energy efficiency and energy conservation are not the same" (6).
"In recent periods, there is no doubt that ICT have played a critical role in reducing energy waste and increasing energy efficiency throughout the economy" (8). Anecdotal evidence is provided.
Huber and Mills wrote in Fortune that PCs use much electricity, and they forecast that trend to continue. However, Koomey concluded that there was only an overall consumption of electricity of about 3% by computers in 2000 as opposed to about 15% by the former authors. The key question that this report attempts to address is not how much energy ICT uses, but what is the net effect of ICT on energy consumption.
There is then much anecdotal evidence that ICT is improving energy efficiency: processers in cars, smart homes, easy business transactions, etc.
The authors conclude with a statistical analysis of the relationship between ICT consumption and energy use, arguing that for every kilowatt-hour of electricity consumed by ICT, 6-14 were saved.
ICT helps to promote energy consumption savings. "For every extra kilowatt-hour of electricity that has been demanded by ICT, the US economy increased its overall energy savings by a factor of about 10" (v). There has been a delinking of economic growth from growth in energy consumption, and this report posits that ICT is a major contributor to this delinking. In addition, it indicates that we are probably not using ICT to its fullest potential.
"There is broad agreement that greater levels of productivity can lead to greater economic returns" (1).
"During the current historical period, gains in productivity are most likely to result from the continued development and application of new information and communications technologies" (1).
There is an outline of how ICT contributes to productivity, relying heavily on Jorgenson et al (2005). Then the topic of energy is explored: "...it does appear that ICT investments may actually be 'energy saving' more broadly speaking. That is, the same digital age investments that are driving a more robust economic productivity are also increasing the efficiency in how we use energy more generally" (3)
"As we implied in the previous section, ICT has not only transformed our economy and our lives, they ahve also reinvigorated economic productivity. What is less well-recognized is that ICT systems have revolutionized the relationship between economic production and energy consumption" (4).
"To more fully explore the ICT paradox and to gain a better understanding of the potential net energy benefits provided by ICT, this report explores the following questions: What is the enabling role of ICT investment and how might they be expanded to increase energy productivity beyond current patterns of improvement? How might a productivity-led ICT strategy provide greater energy security while contributing toward climate change mitigation efforts? What do current energy and efficiency trends look like and how do ICT provide a positive complement within the emerging trends? We begin by assessing where ICT fit within the historical and technological context and by discussing our working definition of the term 'energy efficiency" (4).
"Energy efficiency is a process that achieves the same ends with fewer energy inputs. It's about producing, transporting, traveling, lighting, cooking, heating, and communicating in ways that maintain or increase our productivity for every unit of energy consumed. In other words, energy efficiency is about providing the same goods and services using less energy. Energy efficiency and energy conservation are not the same" (6).
"In recent periods, there is no doubt that ICT have played a critical role in reducing energy waste and increasing energy efficiency throughout the economy" (8). Anecdotal evidence is provided.
Huber and Mills wrote in Fortune that PCs use much electricity, and they forecast that trend to continue. However, Koomey concluded that there was only an overall consumption of electricity of about 3% by computers in 2000 as opposed to about 15% by the former authors. The key question that this report attempts to address is not how much energy ICT uses, but what is the net effect of ICT on energy consumption.
There is then much anecdotal evidence that ICT is improving energy efficiency: processers in cars, smart homes, easy business transactions, etc.
The authors conclude with a statistical analysis of the relationship between ICT consumption and energy use, arguing that for every kilowatt-hour of electricity consumed by ICT, 6-14 were saved.
Monday, January 26, 2009
SMART 2020: Enabling the Low Carbon Economy in the Information Age
The Climate Group on Behalf of the Global eSustainability Initiative (GeSI). 2008. Smart 2020: Enabling the Low Carbon Economy in the Information Age.
This report was compiled by the ICT industry. GeSI is an industry organization that attempts to promote sustainable development through the adoption of ICT technology. This report begins by noting that there are wide ranging goals to reduce carbon emissions to their 1990 levels by 2020. This can be partially accomplished through the further adoption of ICT technologies. This report attempts to show how ICT can be used to accomplish these goals.
"The ICT sector's own emissions are expected to increase, in a business as usual (BAU) scenario, from 0.53 billion tonnes (Gt) carbon dioxide equivalent...in 2002 to 1.43 [billion tonnes] by 2020. But specific ICT opportunities identified in this report can lead to emission reductions five times the size of the sector's own footprint, up to 7.8 [billion tonnes], or 15% of total BAU emissions by 2020" (6).
"Aside from emissions associated with deforestation, the largest contribution to man-made GHG emissions comes from power generation and fuel used for transportation. It is therefore not surprising that the biggest role ICTs could play is in helping to improve energy efficiency in power transmission and distribution...in buildings and factories that demand power and in the use of transportation to deliver goods" (9).
These ICT based savings on carbon emissions, not to mention efficiency improvements and thus other savings, can be achieved most readily in a few, key areas, as identified by this report: Smart Motor Systems; Smart Logistics; Smart Buildings; Smart Grids (9).
Their report draws on IPCC conclusions about the effects of carbon emissions and climate change.
The report makes a claim that, by 2020, ICT will provide for 5 times the reduction in carbon emissions than its own footprint. This is achieved through the following: standardization of energy consumption and emissions; monitoring energy use; accounting improvements relative to energy consumption and emissions; a rethinking in the way that people work, live and play; as well as a transformation through integrating systems (ch 1 pg 15).
ICT represents about 2% of global carbon emissions. "In 2007, the total footprint of the ICIT sector--including personal computers...and peripherals, telecoms networks and devices and data centres--was 830 [metric tons of carbon dioxide], about 2% of the estimated total emissions from human activity released that year. Even if the efficient technology developments outlined in the rest of the chapter are implemented, this figure looks set to grow at 6% each year until 2020"
(ch 2 pg 17).
The relative footprints of personal computers, data centers and telecoms are explored out to 2020.
ICT can help by increasing the efficiency of a variety of sectors within the global economy, from smart grids to improved efficiencies in industrial production. "ICT can make a major contribution to the global response to climate change. It could deliver up to a 15% reduction of BAU emissions in 2020.k..representing a value of [553 billion Euros] in energy and fuel saved and an additional [91 billion Euros] in carbon saved assuming a cost of carbon of [20 Euros/tonne] for a total of [644 billion Euros] savings" (ch 3 pg 51).
The assumptions out to 2020 are the following: elimination of all CDs and DVDs; 3% reduction in emissions from shopping transport; 25% reduction in global paper use; 30% reduction in business air travel for video conferencing; work related travel in urban areas decreased 80%; non work related travel down by 20%; 15% reduction in residential building emissions; 60% decrease in office emissions applied to 80% of office buildings; 30% increase in industrial motor systems; 15% decrease in electricity consumption; 14% reduction in road transport; 24% reduction in inventory; 5% reduction in carbon emissions from lack of congestion; 12% reduction based on improved driving style; 1% reduction in fuel consumption; 32% reduction in ground fuel consumption; 3% reduction in flight time; 2.5% reduction in rail transport b/c of better scheduling; 4% reduction in shipping transport b/c better use of ships; 3% increase in ship performance; 5% reduction in packaging material; 40% reduction in retail buildings; 25% reduction in retail and warehouse space; 13% reduction in HVAC consumption; 16% reduction in lighting; 30% reduction of T&D losses for developed countries and 38% for developing; 5% reduction in energy consumption; 10% reduction in carbon intensity of generation of developed countries; 5% reduction in carbon intensity of generation of developing countries (Appendix 3 pg 66-70).
This report was compiled by the ICT industry. GeSI is an industry organization that attempts to promote sustainable development through the adoption of ICT technology. This report begins by noting that there are wide ranging goals to reduce carbon emissions to their 1990 levels by 2020. This can be partially accomplished through the further adoption of ICT technologies. This report attempts to show how ICT can be used to accomplish these goals.
"The ICT sector's own emissions are expected to increase, in a business as usual (BAU) scenario, from 0.53 billion tonnes (Gt) carbon dioxide equivalent...in 2002 to 1.43 [billion tonnes] by 2020. But specific ICT opportunities identified in this report can lead to emission reductions five times the size of the sector's own footprint, up to 7.8 [billion tonnes], or 15% of total BAU emissions by 2020" (6).
"Aside from emissions associated with deforestation, the largest contribution to man-made GHG emissions comes from power generation and fuel used for transportation. It is therefore not surprising that the biggest role ICTs could play is in helping to improve energy efficiency in power transmission and distribution...in buildings and factories that demand power and in the use of transportation to deliver goods" (9).
These ICT based savings on carbon emissions, not to mention efficiency improvements and thus other savings, can be achieved most readily in a few, key areas, as identified by this report: Smart Motor Systems; Smart Logistics; Smart Buildings; Smart Grids (9).
Their report draws on IPCC conclusions about the effects of carbon emissions and climate change.
The report makes a claim that, by 2020, ICT will provide for 5 times the reduction in carbon emissions than its own footprint. This is achieved through the following: standardization of energy consumption and emissions; monitoring energy use; accounting improvements relative to energy consumption and emissions; a rethinking in the way that people work, live and play; as well as a transformation through integrating systems (ch 1 pg 15).
ICT represents about 2% of global carbon emissions. "In 2007, the total footprint of the ICIT sector--including personal computers...and peripherals, telecoms networks and devices and data centres--was 830 [metric tons of carbon dioxide], about 2% of the estimated total emissions from human activity released that year. Even if the efficient technology developments outlined in the rest of the chapter are implemented, this figure looks set to grow at 6% each year until 2020"
(ch 2 pg 17).
The relative footprints of personal computers, data centers and telecoms are explored out to 2020.
ICT can help by increasing the efficiency of a variety of sectors within the global economy, from smart grids to improved efficiencies in industrial production. "ICT can make a major contribution to the global response to climate change. It could deliver up to a 15% reduction of BAU emissions in 2020.k..representing a value of [553 billion Euros] in energy and fuel saved and an additional [91 billion Euros] in carbon saved assuming a cost of carbon of [20 Euros/tonne] for a total of [644 billion Euros] savings" (ch 3 pg 51).
The assumptions out to 2020 are the following: elimination of all CDs and DVDs; 3% reduction in emissions from shopping transport; 25% reduction in global paper use; 30% reduction in business air travel for video conferencing; work related travel in urban areas decreased 80%; non work related travel down by 20%; 15% reduction in residential building emissions; 60% decrease in office emissions applied to 80% of office buildings; 30% increase in industrial motor systems; 15% decrease in electricity consumption; 14% reduction in road transport; 24% reduction in inventory; 5% reduction in carbon emissions from lack of congestion; 12% reduction based on improved driving style; 1% reduction in fuel consumption; 32% reduction in ground fuel consumption; 3% reduction in flight time; 2.5% reduction in rail transport b/c of better scheduling; 4% reduction in shipping transport b/c better use of ships; 3% increase in ship performance; 5% reduction in packaging material; 40% reduction in retail buildings; 25% reduction in retail and warehouse space; 13% reduction in HVAC consumption; 16% reduction in lighting; 30% reduction of T&D losses for developed countries and 38% for developing; 5% reduction in energy consumption; 10% reduction in carbon intensity of generation of developed countries; 5% reduction in carbon intensity of generation of developing countries (Appendix 3 pg 66-70).
Monday, July 21, 2008
Triplett: The Solow Productivity Paradox
Triplett, JE. 1999. “The Solow Productivity Paradox: What Do Computers Do to Productivity?.” Canadian Journal of Economics 32:309-334.
(1) “You don't see computers 'everywhere,' in a meaningful economic sense. Computers and information processing equipment are a relatively small share of GDP and of the capital stock.
(2) You only thinkyou see computers everywhere. Government hedonic price indexes for computers fall 'too fast,' according to this position, and therefore measured real computer output growth is also 'too fast.'
(3) You may not see computers everywhere, but in the industrial sectors where you most see them, output is poorly measured. Examples are finance and insurance, which are heavy users of information technology and where even the concept of output is poorly specified.
(4) Whether or not you see computers everywhere, some of 'what they do is not counted in economic statistics. Examples are consumption on the job, convenience, better user-interface, and so forth.
(5) You don 't see computers in the productivity statistics yet, but wait a bit and you will. This is the analogy with the diffusion of electricity; the idea that the productivity implications of a new technology are only visible witlh a long lag.
(6) You see computers everywhere but in the productivity statistics because computers are not as productive as you think. Here, there are many anecdotes, such as failed computer system design projects, but there are also assertions from computer science that computer and software design has taken a wrong turn.
(7) There is no paradox. some economists are counting innovations and new products on an arithmetic scale when they should count on a logarithmic scale”
(309)
The author explores the preceding claims relating to the Solow Paradox.
(1) “You don't see computers 'everywhere,' in a meaningful economic sense. Computers and information processing equipment are a relatively small share of GDP and of the capital stock.
(2) You only thinkyou see computers everywhere. Government hedonic price indexes for computers fall 'too fast,' according to this position, and therefore measured real computer output growth is also 'too fast.'
(3) You may not see computers everywhere, but in the industrial sectors where you most see them, output is poorly measured. Examples are finance and insurance, which are heavy users of information technology and where even the concept of output is poorly specified.
(4) Whether or not you see computers everywhere, some of 'what they do is not counted in economic statistics. Examples are consumption on the job, convenience, better user-interface, and so forth.
(5) You don 't see computers in the productivity statistics yet, but wait a bit and you will. This is the analogy with the diffusion of electricity; the idea that the productivity implications of a new technology are only visible witlh a long lag.
(6) You see computers everywhere but in the productivity statistics because computers are not as productive as you think. Here, there are many anecdotes, such as failed computer system design projects, but there are also assertions from computer science that computer and software design has taken a wrong turn.
(7) There is no paradox. some economists are counting innovations and new products on an arithmetic scale when they should count on a logarithmic scale”
(309)
The author explores the preceding claims relating to the Solow Paradox.
Sunday, July 20, 2008
Arthur: Myths and Realities of the High-Tech Economy
Arthur, WB. 2000. “Myths and Realities of the High-Tech Economy.” Talk given at Credit Suisse First Boston Though Leader Forum, Santa Fe Institute.
Author presents 4 myths of the “high tech economy”.
The author first points out a general trend: a digital economy of networks has emerged that is distinct from the earlier economy of input-output production. This new economy is overlaid upon the old economy. Additionally, the author believes that, “of networks, there will be few” because increasing returns to scale will be impossible to overcome with differentiation (1).
Myth 1: All Networks are Subject to Network effects:
“Network effects”, a synonym for Metcalfe’s Law, states that the “value” of networks increases quadratically. Therefore, any network that people are involved within will be beneficial for those people. While this relationship may be true of some technologies (the author points to a telephone), the nature of digital networks provides different kinds of increasing returns with increased membership in a network. Combinatorial networks, like eBay, show some sort of increasing returns to scale, while “hub and spoke” networks do not: users are just connected to the same server and not to one another.
Myth 2: The Coming of the Internet implies a New Economy:
Myth 3: Either that technology must evolve in a specific place, or that technology can come from anywhere: the answer is somewhere in the middle.
Myth 4: Current political structures will last forever
Author presents 4 myths of the “high tech economy”.
The author first points out a general trend: a digital economy of networks has emerged that is distinct from the earlier economy of input-output production. This new economy is overlaid upon the old economy. Additionally, the author believes that, “of networks, there will be few” because increasing returns to scale will be impossible to overcome with differentiation (1).
Myth 1: All Networks are Subject to Network effects:
“Network effects”, a synonym for Metcalfe’s Law, states that the “value” of networks increases quadratically. Therefore, any network that people are involved within will be beneficial for those people. While this relationship may be true of some technologies (the author points to a telephone), the nature of digital networks provides different kinds of increasing returns with increased membership in a network. Combinatorial networks, like eBay, show some sort of increasing returns to scale, while “hub and spoke” networks do not: users are just connected to the same server and not to one another.
Myth 2: The Coming of the Internet implies a New Economy:
Myth 3: Either that technology must evolve in a specific place, or that technology can come from anywhere: the answer is somewhere in the middle.
Myth 4: Current political structures will last forever
Greening et. al.: Energy Efficiency and Consumption
A. Greening, L, DL Greene, and C Difiglio. 2000. “Energy efficiency and consumption: the rebound effect: a survey.” Energy Policy 28:389-401.
Rebound effects evolve from a neoclassical economic assumption. They deal with an increase in energy efficiency causing the price for a comparable good to drop, and thus to increase demand for that good, offsetting the efficiency improvement. The assumption here is that, if increased efficiency is to equal decreased overall energy consumption, it must be accompanied by an increase in energy costs.
Direct effects are seen at the micro-level when there is an improvement in energy efficiency. This reduces the price of a good, and this changes consumer patterns. Another effect happening at a different level of the economy results from changes in these consumer patterns and changes in demand profiles. Another effect, the transformational effect can, “…change consumer preferences, alter social institutions and rearrange the organization of production” (391).
The rest of the article comprises a review of quantitative studies on rebound effects. It would be well worth exploring.
Rebound effects evolve from a neoclassical economic assumption. They deal with an increase in energy efficiency causing the price for a comparable good to drop, and thus to increase demand for that good, offsetting the efficiency improvement. The assumption here is that, if increased efficiency is to equal decreased overall energy consumption, it must be accompanied by an increase in energy costs.
Direct effects are seen at the micro-level when there is an improvement in energy efficiency. This reduces the price of a good, and this changes consumer patterns. Another effect happening at a different level of the economy results from changes in these consumer patterns and changes in demand profiles. Another effect, the transformational effect can, “…change consumer preferences, alter social institutions and rearrange the organization of production” (391).
The rest of the article comprises a review of quantitative studies on rebound effects. It would be well worth exploring.
Plepys: The Grey Side of ICT
Plepys, A. 2002. “The grey side of ICT.” Environmental Impact Assessment Review 22:509-523.
Begins with the argument that ICT is a GPT and that it is transforming economies.
“It is expected that ICTs are capable of delinking the economic growth from environmental degradation primarily due to their potential to increase productivity and create value-added in the form of manipulating ideas and information rather than energy and materials” (510). It is difficult to actually determine the environmental effects of ICT. “Evidence from the energy sector shows that a more efficient use of natural resources does not always reduce their absolute consumption” (510). “A more energy-efficient equipment reduces manufacturing costs and, consequently, the final price of a unit of product or service, which in turn increases demand. The phenomenon, called rebound effect, is well known to energy economists” (510).
“At present, we still know too little about the relation of ICT to the environment. However, the technology has a number of potential risks and uncertainties that we need to understand when placing high expectations on ICT. Drawing a parallel between the rebound effects in the energy sector and ICT helps discussing the environmental implications of the growing ICT use” (510).
“…the term rebound effect refers to an effective increase in the consumption of an energy service after its price decreases due to higher efficiency of the production of the service” (510). “If technological progress makes certain equipment more energy efficient, less energy is needed to produce the same amount of products or services, thus the cost per unit of production falls, which leads to increased demand for the product or the service” (510).
See Greening et. al. (2000) for a four tiered taxonomy of rebound effects that may be thoroughly complex to account for the GPT nature of ICT.
Firstly, there are direct or pure price effects. “These effects occur as a consequence of increased energy efficiency, which reduces the price of energy utilities by decreasing the amount of fuel needed to produce a commodity and, consequently, decreases its final price” (511).
These effects can be broken down into either “substation” or “income effects”. “A consume rwill not increase the use of the ‘bargain’ commodity indefinitely, but until the limits of satiation or budgetary tradeoffs with other expenditures” (511).
There are also “second-order” consumption effects relating to increased consumption power based on savings from purchase of goods that have been reduced in price because of increased efficiency (511).
The third type of effect that is highlighted by Greening et. al. (2000) is that taking place on the whole economy, or, “economy-wide” effects. “The argument of the economy-wide effects builds on the interrelationship of prices and outputs of goods and resources in different markets, which form a unique equilibrium state” (511).
“Transformational effects” are the final effect in this taxonomy. This relates to the, “changes of consumer preferences, altered social institutions, and organization of production” (512).
This taxonomy is applied to ICT.
The price effect is seen in the need to constantly innovate as expectations are that consumers will receive more computing power for the same price as time moves forward. “The phenomenon of receiving more performance for the same price ahs an analogy with the direct rebound effects in the energy sector” (513).
“The official statistics in the United States, indeed, indicated a decoupling between the GDP-measured economic growth and energy consumption. For example, some reports predict that the U.S. ICT sector will grow by 4.0% annually (other sectors only by 2.2%), while its energy intensity will be reduced by 0.92% (EIA, 1999). In the United States, during 1996–1999 the energy intensity per GDP unit declined by 3.4% compared to the decline of 2.6% during the oil crisis. More surprising, the decline of the late 1990s occurred without any significant price signals or policy initiatives (Laitner, 2000; USDC, 2000)” (514). Some credit this delinking with structural changes brought about by ICT innovation and continued adoption.
Further effects of the taxonomy created by ICT adoption are difficult to surmise as they involve complex consumer behavior.
Direct environmental effects:
“One part of environmental impacts derives directly from the life cycle of ICT products. The other part originates from the use of ICT products and services, enhancing or substituting traditional processes or creating new ones. Therefore, when discussing the environmental impacts of ICT, it is useful to frame them into both the live cycle and the system’s perspectives” (515).
One way of exploring the life cycle, is to equate the production with an ecological backpack. This uses the MIPS method of Wuppertal Institute of Germany. Many computers may require material intensity of 16-19 metric tons (516).
There is a general discussion of different technologies and their relative energy consumption needs. For example, it is much more environmentally friendly to produce and lay fiber optic cables in place of copper, but that means that they are laid everywhere (the author cites National Geographic which has them being laid faster than the speed of sound). There is also the problem of faster networks necessitating faster computing, and planned obsolescence (this last term not directly in text, but tacitly so).
Additionally, waste produced by discarded computers annually requires either huge investments in recycling centers or landfills.
ICT has the potential to help create sustainable development, but this is by no means guaranteed. There has to be a change in consumer behavior for this to happen. ICT can simply be a means to improve channels of consumerism.
Some ways in that ICT has failed to promote sustainable development: paperless offices are non-existent: paper consumption continues to rise. Digital Media: users who own computers are not necessarily more efficient in receiving their news. Online consumption: can lower price, increase demand, increase consumption.
“Clearly, the ICT has a potential to decouple economic growth from economic growth from environmental degradation. However, without considering potential rebound effects of increased ICT consumption, the environmental implications can quickly become detrimental. The environmental impacts of ICT largely depend on how the ICT applications perform when human behavior becomes a very important factor” (521).
Begins with the argument that ICT is a GPT and that it is transforming economies.
“It is expected that ICTs are capable of delinking the economic growth from environmental degradation primarily due to their potential to increase productivity and create value-added in the form of manipulating ideas and information rather than energy and materials” (510). It is difficult to actually determine the environmental effects of ICT. “Evidence from the energy sector shows that a more efficient use of natural resources does not always reduce their absolute consumption” (510). “A more energy-efficient equipment reduces manufacturing costs and, consequently, the final price of a unit of product or service, which in turn increases demand. The phenomenon, called rebound effect, is well known to energy economists” (510).
“At present, we still know too little about the relation of ICT to the environment. However, the technology has a number of potential risks and uncertainties that we need to understand when placing high expectations on ICT. Drawing a parallel between the rebound effects in the energy sector and ICT helps discussing the environmental implications of the growing ICT use” (510).
“…the term rebound effect refers to an effective increase in the consumption of an energy service after its price decreases due to higher efficiency of the production of the service” (510). “If technological progress makes certain equipment more energy efficient, less energy is needed to produce the same amount of products or services, thus the cost per unit of production falls, which leads to increased demand for the product or the service” (510).
See Greening et. al. (2000) for a four tiered taxonomy of rebound effects that may be thoroughly complex to account for the GPT nature of ICT.
Firstly, there are direct or pure price effects. “These effects occur as a consequence of increased energy efficiency, which reduces the price of energy utilities by decreasing the amount of fuel needed to produce a commodity and, consequently, decreases its final price” (511).
These effects can be broken down into either “substation” or “income effects”. “A consume rwill not increase the use of the ‘bargain’ commodity indefinitely, but until the limits of satiation or budgetary tradeoffs with other expenditures” (511).
There are also “second-order” consumption effects relating to increased consumption power based on savings from purchase of goods that have been reduced in price because of increased efficiency (511).
The third type of effect that is highlighted by Greening et. al. (2000) is that taking place on the whole economy, or, “economy-wide” effects. “The argument of the economy-wide effects builds on the interrelationship of prices and outputs of goods and resources in different markets, which form a unique equilibrium state” (511).
“Transformational effects” are the final effect in this taxonomy. This relates to the, “changes of consumer preferences, altered social institutions, and organization of production” (512).
This taxonomy is applied to ICT.
The price effect is seen in the need to constantly innovate as expectations are that consumers will receive more computing power for the same price as time moves forward. “The phenomenon of receiving more performance for the same price ahs an analogy with the direct rebound effects in the energy sector” (513).
“The official statistics in the United States, indeed, indicated a decoupling between the GDP-measured economic growth and energy consumption. For example, some reports predict that the U.S. ICT sector will grow by 4.0% annually (other sectors only by 2.2%), while its energy intensity will be reduced by 0.92% (EIA, 1999). In the United States, during 1996–1999 the energy intensity per GDP unit declined by 3.4% compared to the decline of 2.6% during the oil crisis. More surprising, the decline of the late 1990s occurred without any significant price signals or policy initiatives (Laitner, 2000; USDC, 2000)” (514). Some credit this delinking with structural changes brought about by ICT innovation and continued adoption.
Further effects of the taxonomy created by ICT adoption are difficult to surmise as they involve complex consumer behavior.
Direct environmental effects:
“One part of environmental impacts derives directly from the life cycle of ICT products. The other part originates from the use of ICT products and services, enhancing or substituting traditional processes or creating new ones. Therefore, when discussing the environmental impacts of ICT, it is useful to frame them into both the live cycle and the system’s perspectives” (515).
One way of exploring the life cycle, is to equate the production with an ecological backpack. This uses the MIPS method of Wuppertal Institute of Germany. Many computers may require material intensity of 16-19 metric tons (516).
There is a general discussion of different technologies and their relative energy consumption needs. For example, it is much more environmentally friendly to produce and lay fiber optic cables in place of copper, but that means that they are laid everywhere (the author cites National Geographic which has them being laid faster than the speed of sound). There is also the problem of faster networks necessitating faster computing, and planned obsolescence (this last term not directly in text, but tacitly so).
Additionally, waste produced by discarded computers annually requires either huge investments in recycling centers or landfills.
ICT has the potential to help create sustainable development, but this is by no means guaranteed. There has to be a change in consumer behavior for this to happen. ICT can simply be a means to improve channels of consumerism.
Some ways in that ICT has failed to promote sustainable development: paperless offices are non-existent: paper consumption continues to rise. Digital Media: users who own computers are not necessarily more efficient in receiving their news. Online consumption: can lower price, increase demand, increase consumption.
“Clearly, the ICT has a potential to decouple economic growth from economic growth from environmental degradation. However, without considering potential rebound effects of increased ICT consumption, the environmental implications can quickly become detrimental. The environmental impacts of ICT largely depend on how the ICT applications perform when human behavior becomes a very important factor” (521).
Saturday, July 19, 2008
Briscoe, et. al.: Metcalfe's Law is Wrong
Briscoe, B., Odlyzko, A. & Tilly, B., 2006. Metcalfe's law is wrong-communications networks increase in value as they add members-but by how much? Spectrum, IEEE, 43(7), 34-39.
This article discusses why its authors believe that Metcalfe’s Law is over stated and mathematically incorrect. They put forth a log-based account of the law instead of a quadratic account. “Simply put, it [Metcalfe’s Law] says that the value of a communications network is proportional to the square of the number of its users” (35).
The authors attribute the dot-com bubble to people making decisions based upon Metcalfe’s Law instead of a logged version of network analysis.
Sarnoff’s Law was named after an RCA television executive and it dealt with the number of viewers of broadcast networks. It grows linearly.
Their “valuation” can not be proven, oversimplifies the phenomena, and doesn’t make explicit any of the nuanced interactions (both positive and negative).
Their basic objection to Metcalfe’s law is that it values all connections equally when, in reality, many connections are not taken advantage of and are thus not valuable.
There is anecdotal evidence that the logged law is widely applicable.
There is a grand conflation of “value”.
This article discusses why its authors believe that Metcalfe’s Law is over stated and mathematically incorrect. They put forth a log-based account of the law instead of a quadratic account. “Simply put, it [Metcalfe’s Law] says that the value of a communications network is proportional to the square of the number of its users” (35).
The authors attribute the dot-com bubble to people making decisions based upon Metcalfe’s Law instead of a logged version of network analysis.
Sarnoff’s Law was named after an RCA television executive and it dealt with the number of viewers of broadcast networks. It grows linearly.
Their “valuation” can not be proven, oversimplifies the phenomena, and doesn’t make explicit any of the nuanced interactions (both positive and negative).
Their basic objection to Metcalfe’s law is that it values all connections equally when, in reality, many connections are not taken advantage of and are thus not valuable.
There is anecdotal evidence that the logged law is widely applicable.
There is a grand conflation of “value”.
Brynjolfsson: The Productivity Paradox of Information Technology
Brynjolfsson, E., 1993. The productivity paradox of information technology. Communications of the ACM, 36(12), 66-77.
Written in 1993, this article represents an important contribution to the exploration of ICT technology just at the beginning of the internet revolution. The article examines a potential paradox in the deployment of ICT and its expected benefits. Supposively, the increased use of ICT should improve productivity of workers. However, this was not the case in the US at the time of writing. There are four explanations that are given for this: “…outputs and inputs have been mismeasured, that learning and adjustment cause lags, that profits have been redistributed and dissipated, and that information and technology have been mismanaged…” (1).
“After reviewing and assessing the research to date, it appears that the shortfall of IT productivity is as much due to deficiencies in our measurement and methodological tool kit and as to mismanagement by developers and users of IT” (2).
Good review of the studies of productivity and ICT. “A possible explanation of poor measurement of productivity: For example, if a clothing manufacturer chooses to produce more colors and sizes of shirts, which may have value to consumers, existing productivity measures rarely account for such value and will typically show higher "productivity" in a firm that produces a single color and size. Higher prices in industries with increasing product diversity is likely to be attributed to inflation, despite the real increase in value provided to consumers” (8).
And:
“In services, the problem of unmeasured improvements can be even worse than in manufacturing. For instance, the convenience afforded by 24-hour automatic teller machines (ATMs) is a clear example of an unmeasured quality improvement. How much value has this contributed to banking customers? Government statistics implicitly assume it is all captured in the number of transaction, or worse, that output is a constant multiple of labor input!” (8).
Time lags: studies have shown that 2-3 years can be typical before large improvements are found.
Redistributional effects: ICT might not change the size “of the pie”, but it does redistribute the size of the slices.
Written in 1993, this article represents an important contribution to the exploration of ICT technology just at the beginning of the internet revolution. The article examines a potential paradox in the deployment of ICT and its expected benefits. Supposively, the increased use of ICT should improve productivity of workers. However, this was not the case in the US at the time of writing. There are four explanations that are given for this: “…outputs and inputs have been mismeasured, that learning and adjustment cause lags, that profits have been redistributed and dissipated, and that information and technology have been mismanaged…” (1).
“After reviewing and assessing the research to date, it appears that the shortfall of IT productivity is as much due to deficiencies in our measurement and methodological tool kit and as to mismanagement by developers and users of IT” (2).
Good review of the studies of productivity and ICT. “A possible explanation of poor measurement of productivity: For example, if a clothing manufacturer chooses to produce more colors and sizes of shirts, which may have value to consumers, existing productivity measures rarely account for such value and will typically show higher "productivity" in a firm that produces a single color and size. Higher prices in industries with increasing product diversity is likely to be attributed to inflation, despite the real increase in value provided to consumers” (8).
And:
“In services, the problem of unmeasured improvements can be even worse than in manufacturing. For instance, the convenience afforded by 24-hour automatic teller machines (ATMs) is a clear example of an unmeasured quality improvement. How much value has this contributed to banking customers? Government statistics implicitly assume it is all captured in the number of transaction, or worse, that output is a constant multiple of labor input!” (8).
Time lags: studies have shown that 2-3 years can be typical before large improvements are found.
Redistributional effects: ICT might not change the size “of the pie”, but it does redistribute the size of the slices.
O'Fallon: Linkages Between Infrastructure and Economic Growth
O’Fallon, C., 2003. Linkages Between Infrastructure And Economic Growth. unpublished paper prepared for the Ministry of Economic Development, Wellington, New Zealand, December.
“Information and communication technology (ICT) is perhaps better thought of as a technological innovation that is “transforming” economic productivity by shifting economic growth onto a higher level, although the overall slope of the path is expected to remain the same. In some countries, the ICT producing sector has had an impact on the economy-wide average growth, influencing “capital deepening” (increased the intensity of physical capital per unit of labour) and assisting in more efficient work organisation outside of the ICT sector, creating “spillover” gains in productivity in these other sectors. The OECD is proposing to continue to monitor ICT development and its effects. The key action for governments in respect of ICT is to ensure that the macroeconomic policy climate is functioning well to encourage further investment and innovation” (2).
OECD study found that there was an improvement in the quality of labor per unit of capital with the imposition of ICT. Inadequate infrastructure was seen as being a key driver of inequalities in growth among OECD countries. The report also identifies “capital deepening” as being an additional benefit of ICT investment.
The report is a rich overview of infrastructure to economic growth/productivity literature and ICT only represented a small section.
“Information and communication technology (ICT) is perhaps better thought of as a technological innovation that is “transforming” economic productivity by shifting economic growth onto a higher level, although the overall slope of the path is expected to remain the same. In some countries, the ICT producing sector has had an impact on the economy-wide average growth, influencing “capital deepening” (increased the intensity of physical capital per unit of labour) and assisting in more efficient work organisation outside of the ICT sector, creating “spillover” gains in productivity in these other sectors. The OECD is proposing to continue to monitor ICT development and its effects. The key action for governments in respect of ICT is to ensure that the macroeconomic policy climate is functioning well to encourage further investment and innovation” (2).
OECD study found that there was an improvement in the quality of labor per unit of capital with the imposition of ICT. Inadequate infrastructure was seen as being a key driver of inequalities in growth among OECD countries. The report also identifies “capital deepening” as being an additional benefit of ICT investment.
The report is a rich overview of infrastructure to economic growth/productivity literature and ICT only represented a small section.
Bannister and Remenyi: The Societal Value of ICT
Bannister, F. & Remenyi, D., 2003. The Societal Value of ICT: First Steps Towards an Evaluation Framework. Electronic Journal of Information Systems Evaluation, 6(2), 197-206.
Finds a variety of benefits that arise from ICT: Economic (though there isn’t a consensus on this), educational (not just distance learning, but information access), convenience, entertainment, communication, access to information, political and democratic, reduction of risk, ecological, information ecology (203-4).
Then highlights “disbenefits”: information overload, unwanted information, information inaccuracy, new types of crime, collateral economic damage (where crime undermines something broader), vulnerability (society dependent on complex systems), disruption and displacement, destruction of social capital, increased economic competition, reduction or threats to civil liberties, globalization of culture, lower quality of employment and health risk (204-5).
“This paper has not provided definitive method with which to evaluate the information society. Its primary purpose was to explore the extent of the problems involved in this endeavour” (205).
Finds a variety of benefits that arise from ICT: Economic (though there isn’t a consensus on this), educational (not just distance learning, but information access), convenience, entertainment, communication, access to information, political and democratic, reduction of risk, ecological, information ecology (203-4).
Then highlights “disbenefits”: information overload, unwanted information, information inaccuracy, new types of crime, collateral economic damage (where crime undermines something broader), vulnerability (society dependent on complex systems), disruption and displacement, destruction of social capital, increased economic competition, reduction or threats to civil liberties, globalization of culture, lower quality of employment and health risk (204-5).
“This paper has not provided definitive method with which to evaluate the information society. Its primary purpose was to explore the extent of the problems involved in this endeavour” (205).
Wednesday, July 16, 2008
Guerrieri and Paodoan: Modelling ICT as a GPT
Guerrieri, Paolo, and Pier Carlo Paodoan. 2007. Modelling ICT as a General Purpose Technology: Evaluation Models and Tools for Assessment of Innovation and Sustainable Development at the EU Level. Collegium no. 35.
This report was done in an attempt to further the Lisbon Agenda, specifically emphasizing the improved usage of ICT. “Most of the existing models linking ICT to economic growth and employment, both at the macro and the micro level, do not provide a fully satisfactory analysis of the transmission mechanism of ICT to economic performance and do not take fully into account the response of different national systems of production and organization, including the role of business services, to the development and diffusion of the new technologies” (6).
“More progress is also needed in the analysis and empirical investigation of the determinants of ICT spending, which is largely if not completely determined by the private sector” (6).
“Finally, data limitations and the lack of appropriate sets of indicators have been a severe constraint in modeling the impact of ICT related policies” (7).
General Purpose Technologies are: pervasive, technologically dynamic, exponentially impacts productivity (7). ICTs are a GPT. Three ways that it impacts the economy: ICT production; ICT as capital input (importance of computers and information technology as an input in other industries); ICT as special capital input (positive externalities resulting form ICT investment) (8).
Many of the effects of ICT investment are indirect and long-term (8).
Data: Best source is EUROSTAT, especially for human capital information. Time series short. GGDC is a good source for ICT variables and indicators, come from national stats, etc. OECD data have many missing variables, data points.
A “Core Dataset” is then created using a hybrid of GGDC and STAN OECD numbers.
Use 8 CGE models to explore ICT. Find IFs to be quite useful, as it is the only model that explicitly incorporates an ICT sector.
“We have chosen the IFs model to perform simulation exercises, since it includes a specific ICT sector and it allows CIT to exert its impact on the economy via different channels, rather than being modeled as a simple input in a standard production function” (13). They consider a total of 6 different scenarios (13).
A list of their results can be found (i-vi) on p. 14.
“A report by Indepen (2005) underlined that simply increasing total investment in ICT will, in itself, not deliver improvements in productivity and economic growth. To be productive, this investment also requires complementary changes in the way organizations are structured and function as well as in human capital” (15). There is a list of 5 of these different catalyzing factors on page 15.
“Modelling ICT as a GPT requires a multi step procedure” (19):
-when related to TFP, relationship between GPT and ICT; relationship between ICT and economic structure and relationship between ICT and facilitating environment/structure
-these elements interact in a virtuous cycle
Ch. II: ICT as a General Purpose Technology
This chapter is a literature review that situates ICT within the GPT literature, as well as presenting a framework for thinking about a modified production function with a ICT spill-over effects being represented.
“One of the fundamental insights provided by Schumpter (1939) is that technological innovations are not evenly distributed over countries, industries and time” (22). This is then deployed by Perez (mostly) and Freeman to explain techno-economic paradigms. This is explained in depth on 23-4.
GPT: “GPTs are radical new ideas or techniques that have the potential for important impacts on many industries in an economy” (24).
Techno-economic paradigms are compared to GPTs by Helpman (1998). Helpman concludes that, “…the concept of TEP is much broader than that of GPT since it covers the entire economic system surrounding any set of pervasive technologies actually in use” (24).
ICT -> economic production: “…innovation is a complex phenomenon that goes beyond an increase in R&D expenditures and whose impact cannot be understood in the framework of a production function” (25).
A neoclassical aggregate production function is compared with a Structuralist-Evolutionary (S-E) decomposition. The later incorporates a more nuanced and structural account of technology mediated through policy environment affecting productivity and altering performance (26).
“In particular our aim is to shed some light into the black box of the production function and to capture the interactions between ICT, the facilitating structure, public policy and performance” (29). Their assumptions: ICT impact depends on nature of environment; facilitating structure comprised of human capital, regulation and sectoral composition of an economy; certain economic sectors have more impact; R&D is crucial; and ICTs are pervasive technologies (29).
ICT affects production, according to Schreyer (2000) in three ways: production, capital input and special input. The most important influenced occurs through spill-over effects. These effects the authors attempt to replicate through a production function. “In fact spillover…effects of ICT on GDP have been shown to be larger than direct effects” (39).
Ch. III: Information Society and E-Inclusion: Data and Indicators
The goal of this chapter is to overview different data sources and create a core dataset. “Another goal is to explore available data in order to asses which type of information can be ‘consistently’ used in the implementation of multivariate studies on ICT diffusion” (40).
”In the literature on economic growth three effects of ICT on productivity and growth are distinguished. First, ICT investment contributes to overall capital depending thus helping to raise labour productivity. Second, fast technological progress in the production of ICT goods and services may contribute to more rapid multifactor productivity (MFP)_ growth in the ICT producing sector. Third, larger use of ICT may help firms increase their overall efficiency and thus raise MFP” (41).
The following data bases were screened: “…Total Economy Growth Accounting and the 60-inidustry database of the Groningen Growth and Development Center (GGDC); the OECT STAN database for Industrial Analysis and the OECT Main Science and Technology Indicators (MSTI); EUROSTAT, R&D and National Accounts database…In principle, the best coverage in terms of variables/ indicators and countries is given by EUROSTAT data. Time-series length however is too often below 10 years…” (43).
Data sources evaluated on 44: the best overall source is EUROSTAT, especially for human capital and policy variables, however, time series are too short; GGDC is the best for ICT variables and indicators; OECT data have many holes, but are good and straight forward (44).
The authors then develop a “Core Data Set” which is a combination of both OECD and GGDC numbers.
There is some statistical analysis of the Core Data Set, and one of the main findings is a strong relationship between IT expenditures and regulations. More detailed analysis of the results on page 56.
A discussion of e-inclusions follows, with its definition and the reason that it is an important factor for European development. These discussions revolve around the literature on the digital divide. This then moves into discussions of digital literacy, how e-inclusion motivates and empowers, ehealth, egovernment, e-everything.
E-inclusion variables are listed in a table on 75-78.
Ch IV: Technology and Performance
“The purpose of this chapter is twofold. On the one hand, the evaluation will be used to choose the CGE model to be employed for simulation exercises of the impact of ICT on economic performance; on the other hand it will help identifying [sic] possible ways of introducing ICT in several CGE models…” (83).
Heavy focus on how ICT can be modeled through endogenous productivity in a CGE with an emphasis on spill-over effects. Definition of ICT: “…the OECD provides an official definition of ICT, and ICT goods and services are evaluated taking into account their ‘intrinsic‘ quality” (84). The authors also defined productivity as relating to TFP contributions by the ICT sectors. They explain that it is easier to identify positive productivity contributions made by ICT in the production sector, but much more difficult in the service sector. They also identify different types of time lag patterns associated with different structural factors within countries that make them more or less likely to reap the rewards of improved and increased use of ICT.
They then compare 8 models: NEMISIS; MULTIMOD; WORLDSCAN; QUEST; NiGEM; IFs; Oxford World Macroeconomic Model and GEM E-3. They felt like IFs was quite suitable for their purposes because it has a thick endogenous technology model as well as an explicit ICT sector, as well as an ability to alter productivity through R&D spending.
They also claim that IFs does not take into consideration technological spillover effects (102).
They then run simulations with many of the models and explore the various assumptions of each model.
Ch V: ICT as a General Purpose Technology
“The existing CGE models with endogenous technical change cannot fully capture tehh complex mechanisms through which ICT influences economic performance….can be improved in three ways…First, the list of endogenous variables that are affected by ICT could be extended by including employment and international competitiveness. Second, the interaction between ICT and other variables affecting the overall business environment (such as regulation, sectoral composition, and human capital) could be taken into account. Third, the uneven impact of ICT across sectors could be considered as well” (115). Also, claim that a higher degree of sectoral disaggregating would make it easier to model the effects of ICT.
They want to explore a variety of variables relating to ICT and see how this effects productivity differently. They run scenarios with IFs that firstly look at variables that more directly drive production, and then at diffusion effects (119).
There is a review of the structure of the IFs model generally, with a focus on the drivers of MFP.
Scenario A: The role of capital accumulation in the ICT sector. “This scenario aims at understanding the effect of a more productive capital factor in the ICT sector” (124). Conclusion: there is a long time lag.
Scenario B: The diffusion process of ICT best practices across sectors. “The scenario tries to evaluate the impact of a more rapid rate of adoption and diffusion of ICT through the service sector, which is typically a large user of ICT” (128).
Scenario C: The diffusion of ICT across countries. “In this scenario we assume different regimes for the MFP of the ICT sector for technological leaders, in order to evaluate the diffusion of different technological frontiers reached by a country which is assumed to be a system leader” (132).
Scenario D: The effects of past adoptions of ICT. “In this scenario we simulate different regimes regarding the adoption of ICT technologies in the economic system” (137). This scenario focuses heavily on the number of networked people variable in IFs.
Scenario E: The role of institutional setup I. “..effects of a change in the elasticity of the multifactor productivity of the ICT sector to the level of network infrastructure in the economic system” (141).
Scenario F: The role of institutional setup II. “…change the value of the elasticity of MFP of the ICT sector to the infrastructures in communication technology” (143).
Ch. VI: Endogenizing ICT
Exploration of the digital divide and literature explaining the phenomena with different drivers highlighted: Income? Human Capital? Regulation? Demographics? Economic Freedom? Service/Agriculture sectors of the economy? Price index?
“Our empirical results are in line with the literature on the digital divide…Human capital, and investments in R&D are factors that increase ICT investments, while burdensome regulation tends to depress them. Furthermore, the structure of the economy turns out to be a relevant factor to understand the different rate of investment in ICT; in particular, countries with a higher share of the service sector usually display hither ICT investment” (156).
This report was done in an attempt to further the Lisbon Agenda, specifically emphasizing the improved usage of ICT. “Most of the existing models linking ICT to economic growth and employment, both at the macro and the micro level, do not provide a fully satisfactory analysis of the transmission mechanism of ICT to economic performance and do not take fully into account the response of different national systems of production and organization, including the role of business services, to the development and diffusion of the new technologies” (6).
“More progress is also needed in the analysis and empirical investigation of the determinants of ICT spending, which is largely if not completely determined by the private sector” (6).
“Finally, data limitations and the lack of appropriate sets of indicators have been a severe constraint in modeling the impact of ICT related policies” (7).
General Purpose Technologies are: pervasive, technologically dynamic, exponentially impacts productivity (7). ICTs are a GPT. Three ways that it impacts the economy: ICT production; ICT as capital input (importance of computers and information technology as an input in other industries); ICT as special capital input (positive externalities resulting form ICT investment) (8).
Many of the effects of ICT investment are indirect and long-term (8).
Data: Best source is EUROSTAT, especially for human capital information. Time series short. GGDC is a good source for ICT variables and indicators, come from national stats, etc. OECD data have many missing variables, data points.
A “Core Dataset” is then created using a hybrid of GGDC and STAN OECD numbers.
Use 8 CGE models to explore ICT. Find IFs to be quite useful, as it is the only model that explicitly incorporates an ICT sector.
“We have chosen the IFs model to perform simulation exercises, since it includes a specific ICT sector and it allows CIT to exert its impact on the economy via different channels, rather than being modeled as a simple input in a standard production function” (13). They consider a total of 6 different scenarios (13).
A list of their results can be found (i-vi) on p. 14.
“A report by Indepen (2005) underlined that simply increasing total investment in ICT will, in itself, not deliver improvements in productivity and economic growth. To be productive, this investment also requires complementary changes in the way organizations are structured and function as well as in human capital” (15). There is a list of 5 of these different catalyzing factors on page 15.
“Modelling ICT as a GPT requires a multi step procedure” (19):
-when related to TFP, relationship between GPT and ICT; relationship between ICT and economic structure and relationship between ICT and facilitating environment/structure
-these elements interact in a virtuous cycle
Ch. II: ICT as a General Purpose Technology
This chapter is a literature review that situates ICT within the GPT literature, as well as presenting a framework for thinking about a modified production function with a ICT spill-over effects being represented.
“One of the fundamental insights provided by Schumpter (1939) is that technological innovations are not evenly distributed over countries, industries and time” (22). This is then deployed by Perez (mostly) and Freeman to explain techno-economic paradigms. This is explained in depth on 23-4.
GPT: “GPTs are radical new ideas or techniques that have the potential for important impacts on many industries in an economy” (24).
Techno-economic paradigms are compared to GPTs by Helpman (1998). Helpman concludes that, “…the concept of TEP is much broader than that of GPT since it covers the entire economic system surrounding any set of pervasive technologies actually in use” (24).
ICT -> economic production: “…innovation is a complex phenomenon that goes beyond an increase in R&D expenditures and whose impact cannot be understood in the framework of a production function” (25).
A neoclassical aggregate production function is compared with a Structuralist-Evolutionary (S-E) decomposition. The later incorporates a more nuanced and structural account of technology mediated through policy environment affecting productivity and altering performance (26).
“In particular our aim is to shed some light into the black box of the production function and to capture the interactions between ICT, the facilitating structure, public policy and performance” (29). Their assumptions: ICT impact depends on nature of environment; facilitating structure comprised of human capital, regulation and sectoral composition of an economy; certain economic sectors have more impact; R&D is crucial; and ICTs are pervasive technologies (29).
ICT affects production, according to Schreyer (2000) in three ways: production, capital input and special input. The most important influenced occurs through spill-over effects. These effects the authors attempt to replicate through a production function. “In fact spillover…effects of ICT on GDP have been shown to be larger than direct effects” (39).
Ch. III: Information Society and E-Inclusion: Data and Indicators
The goal of this chapter is to overview different data sources and create a core dataset. “Another goal is to explore available data in order to asses which type of information can be ‘consistently’ used in the implementation of multivariate studies on ICT diffusion” (40).
”In the literature on economic growth three effects of ICT on productivity and growth are distinguished. First, ICT investment contributes to overall capital depending thus helping to raise labour productivity. Second, fast technological progress in the production of ICT goods and services may contribute to more rapid multifactor productivity (MFP)_ growth in the ICT producing sector. Third, larger use of ICT may help firms increase their overall efficiency and thus raise MFP” (41).
The following data bases were screened: “…Total Economy Growth Accounting and the 60-inidustry database of the Groningen Growth and Development Center (GGDC); the OECT STAN database for Industrial Analysis and the OECT Main Science and Technology Indicators (MSTI); EUROSTAT, R&D and National Accounts database…In principle, the best coverage in terms of variables/ indicators and countries is given by EUROSTAT data. Time-series length however is too often below 10 years…” (43).
Data sources evaluated on 44: the best overall source is EUROSTAT, especially for human capital and policy variables, however, time series are too short; GGDC is the best for ICT variables and indicators; OECT data have many holes, but are good and straight forward (44).
The authors then develop a “Core Data Set” which is a combination of both OECD and GGDC numbers.
There is some statistical analysis of the Core Data Set, and one of the main findings is a strong relationship between IT expenditures and regulations. More detailed analysis of the results on page 56.
A discussion of e-inclusions follows, with its definition and the reason that it is an important factor for European development. These discussions revolve around the literature on the digital divide. This then moves into discussions of digital literacy, how e-inclusion motivates and empowers, ehealth, egovernment, e-everything.
E-inclusion variables are listed in a table on 75-78.
Ch IV: Technology and Performance
“The purpose of this chapter is twofold. On the one hand, the evaluation will be used to choose the CGE model to be employed for simulation exercises of the impact of ICT on economic performance; on the other hand it will help identifying [sic] possible ways of introducing ICT in several CGE models…” (83).
Heavy focus on how ICT can be modeled through endogenous productivity in a CGE with an emphasis on spill-over effects. Definition of ICT: “…the OECD provides an official definition of ICT, and ICT goods and services are evaluated taking into account their ‘intrinsic‘ quality” (84). The authors also defined productivity as relating to TFP contributions by the ICT sectors. They explain that it is easier to identify positive productivity contributions made by ICT in the production sector, but much more difficult in the service sector. They also identify different types of time lag patterns associated with different structural factors within countries that make them more or less likely to reap the rewards of improved and increased use of ICT.
They then compare 8 models: NEMISIS; MULTIMOD; WORLDSCAN; QUEST; NiGEM; IFs; Oxford World Macroeconomic Model and GEM E-3. They felt like IFs was quite suitable for their purposes because it has a thick endogenous technology model as well as an explicit ICT sector, as well as an ability to alter productivity through R&D spending.
They also claim that IFs does not take into consideration technological spillover effects (102).
They then run simulations with many of the models and explore the various assumptions of each model.
Ch V: ICT as a General Purpose Technology
“The existing CGE models with endogenous technical change cannot fully capture tehh complex mechanisms through which ICT influences economic performance….can be improved in three ways…First, the list of endogenous variables that are affected by ICT could be extended by including employment and international competitiveness. Second, the interaction between ICT and other variables affecting the overall business environment (such as regulation, sectoral composition, and human capital) could be taken into account. Third, the uneven impact of ICT across sectors could be considered as well” (115). Also, claim that a higher degree of sectoral disaggregating would make it easier to model the effects of ICT.
They want to explore a variety of variables relating to ICT and see how this effects productivity differently. They run scenarios with IFs that firstly look at variables that more directly drive production, and then at diffusion effects (119).
There is a review of the structure of the IFs model generally, with a focus on the drivers of MFP.
Scenario A: The role of capital accumulation in the ICT sector. “This scenario aims at understanding the effect of a more productive capital factor in the ICT sector” (124). Conclusion: there is a long time lag.
Scenario B: The diffusion process of ICT best practices across sectors. “The scenario tries to evaluate the impact of a more rapid rate of adoption and diffusion of ICT through the service sector, which is typically a large user of ICT” (128).
Scenario C: The diffusion of ICT across countries. “In this scenario we assume different regimes for the MFP of the ICT sector for technological leaders, in order to evaluate the diffusion of different technological frontiers reached by a country which is assumed to be a system leader” (132).
Scenario D: The effects of past adoptions of ICT. “In this scenario we simulate different regimes regarding the adoption of ICT technologies in the economic system” (137). This scenario focuses heavily on the number of networked people variable in IFs.
Scenario E: The role of institutional setup I. “..effects of a change in the elasticity of the multifactor productivity of the ICT sector to the level of network infrastructure in the economic system” (141).
Scenario F: The role of institutional setup II. “…change the value of the elasticity of MFP of the ICT sector to the infrastructures in communication technology” (143).
Ch. VI: Endogenizing ICT
Exploration of the digital divide and literature explaining the phenomena with different drivers highlighted: Income? Human Capital? Regulation? Demographics? Economic Freedom? Service/Agriculture sectors of the economy? Price index?
“Our empirical results are in line with the literature on the digital divide…Human capital, and investments in R&D are factors that increase ICT investments, while burdensome regulation tends to depress them. Furthermore, the structure of the economy turns out to be a relevant factor to understand the different rate of investment in ICT; in particular, countries with a higher share of the service sector usually display hither ICT investment” (156).
Wednesday, February 13, 2008
Hofkirchner et. al.: ICTs and Society: The Salzburg Approach
Hofkirchner, Wolfgang, Fuchs, Christian, Raffl, Celina, Schafranek, Matthias, Sandoval, Marisol, & Bichler, Robert (2007). ICTs and Society: The Salzburg Approach. Towards a Theory for, about, and my means of the Information Society
There is an increasingly important role being played by ICT in the social sciences. This article, in part, attempts to identify exactly what that role should be. It argues that ICT should be understood as a “transdiscipline”. It is a unique position taken by the
“By the term transdiscipline we mean something distinct from the term inter-discipline. This in two respects: first in the respect of the scientific status and second in the respect of the societal function” (7). In regards to the scientific status referred to in the above quote, the transdiscipline nature of ICT should be a transgression of different barriers that have been historically established between disciplines. This should also work as a force to “bridge several gaps” that exist between both natural and social sciences, involving the gap between specialists and generalist and the gap between basic and applied research. Regarding the societal function discussed in the quote at the beginning of this paragraph, “…the concept of a transciscipline does not adhere to the long held assumption of science being in an ivory tower, but implies a transgression from the scientist to the stakeholders affected by the results of research and a transformation into a new science that is human-centered, democratic, participatory” (12).
The creation of the Information and Communication Technologies & Society (ICT&S) at the University of Salzburg will pursue the following in a social context: “…develops both technological prototypes and policy recommendations for the implementation of ICTs for business, government, and civil society organizations…” and “…researches the design of ICTs as well as the design of the full range of societal…conditions of their implementation” and “…crosses the perspectives of engineering and social sciences and humanities and even other disciplines if appropriate” (19). The institution is dedicated to studying and approaching the interaction of technology and society as, “…a feedback-loop between science and technology, no the one hand, and society on the other, in times when the old modes of steering society have proven obsolete” (24). The eventual goal is to bring about a Global Sustainable Information Society (GSIS).
The GSIS involves sustainability, justice, fairness, equality, solidarity, harmony with nature, freedom, human efficiency, culture, politics, economy, technology, etc.
There is a further fleshing out of the scope, aims and tools that ICT&S will use to achieve GSIS, with an extensive discussion of the internet as a complex organization. The report concludes by reiterating the goal of the report, which was to make the case that ICT must be a transdisciplinary approach to tackling the complex problems that current society faces.
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