Anon. 2009. Climate Change and Energy Insecurity: The Challenge for Peace, Security and Development. London: Earthscan.
Edited volume of short chapters dealing with many aspects of climate change, clearly not just with energy insecurity.
Some chapters that may have limited use:
Oil: How Can Europe Kick the Habit of Dependence?
Derek Osborn
“Two critical factors will shape the future of oil production and consumption over the next decades. The first is accelerating climate change. The second is the increasing difficulty in finding secure sources of oil in the world. The interaction of these two factors is currently leading the world into a more and more unstable position” (18).
Localized Energy Conflicts in the Oil Sector
Nniimmo Bassey
Oil corrupts already corrupted states. Energy conflicts are not local, but are global (50). What about national peak oils, not just global peak oil (51).
Climate of Fear: Environment, Migration and Security
Devyani Gupta
This is a problem.
Showing posts with label Energy. Show all posts
Showing posts with label Energy. Show all posts
Monday, January 25, 2010
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
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).
Sunday, July 20, 2008
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).
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