smart everything: will intelligent systems reduce resource ... 004/2013_koomey... ·...

EG38CH12-Koomey ARI 16 September 2013 12:59

Smart Everything: WillIntelligent Systems ReduceResource Use?Jonathan G. Koomey,1 H. Scott Matthews,2

and Eric Williams3

1Steyer-Taylor Center for Energy Policy and Finance, Stanford University, Stanford,California 94305; email: [email protected] of Civil and Environmental Engineering, Department of Engineering andPublic Policy, and Green Design Institute, Carnegie Mellon University, Pittsburgh,Pennsylvania 15213-3890; email: [email protected] Institute for Sustainability, Rochester Institute of Technology, Rochester,New York 14623-5603; email: [email protected]

Annu. Rev. Environ. Resour. 2013. 38:311–43

The Annual Review of Environment and Resources isonline at http://environ.annualreviews.org

This article’s doi:10.1146/annurev-environ-021512-110549

Copyright c© 2013 by Annual Reviews.All rights reserved

Keywords

information technology, information and communicationstechnologies, mobile sensors, energy efficiency of computing, energyharvesting, energy storage, microelectromechanical systems (MEMS)

Abstract

Until recently, the main environmental concerns associated with infor-mation and communication technologies (ICTs) have been their use-phase electricity consumption and the chemicals associated with theirmanufacture, and the environmental effects of these technologies onother parts of the economy have largely been ignored. With the ad-vent of mobile computing, communication, and sensing devices, theseindirect effects have the potential to be much more important than theimpacts from the use and manufacturing phases of this equipment. Thisarticle summarizes the trends that have propelled modern technologicalsocieties into the ultralow-power design space and explores the implica-tions of these trends for the direct and indirect environmental impactsassociated with these new technologies. It reviews the literature on en-vironmental effects of information technology (also with an emphasison low-power systems) and suggests areas for further research.

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Contents

INTRODUCTION. . . . . . . . . . . . . . . . . 313FACTORS DRIVING

ADOPTION OF MOBILEINFORMATIONAND COMMUNICATIONTECHNOLOGIES . . . . . . . . . . . . . . 314The Relationship Between

Conventional andLow-Energy Information andCommunicationTechnologies . . . . . . . . . . . . . . . . . 314

Efficiency of Low-EnergyComputing . . . . . . . . . . . . . . . . . . . 315

Efficiency of WirelessCommunications . . . . . . . . . . . . . . 319

Trends in Energy Storage. . . . . . . . . 320New Developments in Sensors

and Controls . . . . . . . . . . . . . . . . . . 322Trends in Energy Collection

and Transfer Technologies . . . . 323Relationships Between Power Use,

Energy Storage,and Energy Harvesting . . . . . . . . 324

Communication Protocols . . . . . . . . 326THE EFFECTS OF

INFORMATIONTECHNOLOGY ONRESOURCE USE . . . . . . . . . . . . . . . 328Use-Phase Electricity

Consumptionof Information andCommunication TechnologyEquipment . . . . . . . . . . . . . . . . . . . . 328

Manufacturing Energy andEmissions Associated withInformation andCommunication TechnologyEquipment . . . . . . . . . . . . . . . . . . . . 329

Information andCommunication Technologyand Economic Efficiency. . . . . . . 331

Capabilities Enabled byInformation andCommunicationTechnology . . . . . . . . . . . . . . . . . . . 332

Information and CommunicationTechnology and EnergyConsumption in the WholeEconomy . . . . . . . . . . . . . . . . . . . . . 333

Information and CommunicationTechnology and BroaderMeasures of Consumption . . . . . 334

OPEN RESEARCHQUESTIONS . . . . . . . . . . . . . . . . . . . 334Direct Electricity Used by

Information andCommunicationTechnologies . . . . . . . . . . . . . . . . . 334

Effect of Informationand CommunicationTechnology on the BroaderEconomy . . . . . . . . . . . . . . . . . . . . . 335

Quantifying Network Electricityand Emissions Associatedwith Data Transfers . . . . . . . . . . . 335

Possible Rebound Effects in DataCenters . . . . . . . . . . . . . . . . . . . . . . . 335

Comparison of CurrentWireless Data TransmissionEfficiencies toPhysical Limits . . . . . . . . . . . . . . . . 335

Achieving Even Lower-PowerLevels for Information andCommunicationTechnologies . . . . . . . . . . . . . . . . . 336

Identifying New Applicationsfor Low-Power Informationand CommunicationTechnologies . . . . . . . . . . . . . . . . . 336

Creating New Analytical Toolsto Cope with the Inrushof New Data . . . . . . . . . . . . . . . . . . 336

CONCLUSIONS. . . . . . . . . . . . . . . . . . . 336

312 Koomey · Matthews ·Williams

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General-purposetechnology:a technology thatcatalyzes and enablesbroad and deepstructural changesthroughout theeconomy, e.g., theinternal combustionengine

ICTs: informationand communicationtechnologies

Moore’s law: anempirical observationfrom Gordon Mooreof Intel in 1965 thatthe number ofcomponents on a chipwould double everyyear (changed to every2 years in 1975)

PC: personalcomputer

GHz: gigahertz(billions of cycles persecond)

INTRODUCTION

Innovation in modern industrial societies hasbeen driven in the past two centuries by a seriesof what economists call “general-purpose tech-nologies,” which have far-ranging effects on theway the economy produces value. The most im-portant of these were the steam engine, the elec-tric power grid, the internal combustion engine,and, in the past few decades, information andcommunication technologies (ICTs) (1, 2).

Until recently, the wider use of ICTshas mainly been driven by rapid reductionsin the cost of such equipment (3). Thosecost reductions have resulted in widespreadadoption, with important implications for thestructure of businesses and the ways consumersaccess information (4).

The technological factors enablingMoore’s law (5), which projected a doubling ofcomponent densities every year after 1965 anda doubling every two years after 1975, havebeen important drivers of these cost reductionssince the 1960s. However, the recent pushtoward “smart” devices (i.e., those with embed-ded computing and wireless communications)owes more to a parallel set of trends in the effi-ciency of computing and communications, thedevelopment of new sensor and control tech-nologies, and improvements in energy storageand harvesting. These trends have led to anew class of computing devices that are small,cheap, connected, and so low power that theycan operate for long periods using batteries orpower scavenged from ambient energy flows.

This design challenge is very different fromthat for earlier computing devices. Some ofour most talented engineers have for decadesbeen drawn to building the biggest supercom-puter or the fastest personal computer (PC),and the innovations to achieve those goals re-volve around maximizing performance. Nowour fastest processors have clock speeds in thegigahertz (GHz) range, and large computingsystems can have tens or hundreds of thousandsof computing cores operating in parallel.

In contrast, creating low-power devices re-quires attention to minimizing standby as well

as active power and an intense investigationof ways to communicate in ever more effi-cient ways. Because these devices are small andcheap, they can be embedded almost every-where, which allows us to apply computingpower and communications in ways that no-body could have imagined decades ago. In rawperformance terms, these devices cannot matcha PC, but in many applications, processors withrelatively low performance are adequate as longas their average energy use over time is lowenough to fit within a tight power envelope.

These ultralow-power devices have thepotential to revolutionize our ability to un-derstand and respond to the world aroundus. Consider the low-power communicationtechnology that Proteus Biomedical (http://www.proteusbiomed.com) now embeds inpharmaceuticals (6). This 1-cubic-millimeterdevice has no battery; it has an anode and acathode, and it uses the patient’s stomach juicesas the electrolyte. When ingested, the pill sendsa low-power signal to a patch on the patient’sskin, and the patch relays the signal to a mobilephone or similar device. This signal recordswhen the patient takes the medicine, whichmatters greatly for certain patient populations.Later incarnations of this technology will relaysensor data as well, and they open up thepossibility of truly personalized medicine.

The amount of computing performed bysuch a device is tiny, but the value of theinformation delivered is orders of magnitudehigher than the cost of manufacturing the de-vice. Technology delivering such value can be acatalyst for truly disruptive innovation through-out the economy.

The advent of ICTs has seen growth in thedirect electricity used by computing equipmentas well as changes in the overall efficiency andconsumption in the broader economy. This ar-ticle reviews the literature on the direct andindirect energy and environmental effects ofICTs, with an emphasis on the transition toultralow-power systems. Although it is impos-sible to estimate many of these effects quanti-tatively, it is important to characterize them in

www.annualreviews.org • Smart Everything 313

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http://www.proteusbiomed.com

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International DataCorporation:a widely referencedcollector of data on theinformationtechnology industry

a consistent and accurate way, and to identifyfruitful areas of further research.

FACTORS DRIVING ADOPTIONOF MOBILE INFORMATIONAND COMMUNICATIONTECHNOLOGIES

A confluence of improvements in the costsand efficiency of computing, communications,sensors, energy harvesting, and energy stor-age technologies has led to a vast increase inthe numbers and types of ICT equipment,with consequent improvements in our abilityto monitor changes in institutions, technologi-cal systems, and the environment in real time.This section reviews data on these trends andsummarizes their implications.

The Relationship BetweenConventional and Low-EnergyInformation and CommunicationTechnologies

The history of computing has until recently fo-cused on the rise of general-purpose computingdevices,1 such as PCs and servers (3). As com-puting becomes more distributed and more mo-bile, PCs are diminishing in importance. Forexample, laptops passed desktop PCs in annualunit shipments worldwide for the first time in2009 according to the International Data Cor-poration (7). And now, tablet computers (e.g.,the iPad) are starting to eat away at the marketshare of both desktops and laptops, with tabletscapturing almost 25% market share worldwidein late 2012 (8).

This trend is reflected also in the shift to-ward ultralow-power computing and sensors(9). PCs are occasionally used as part of the data

1The concept of general-purpose computing is different fromthat of general-purpose technologies described earlier in thisarticle. General-purpose computers can be programmed toassess many different kinds of problems. Special-purpose ma-chines can sometimes be created that can solve particularclasses of problems much more efficiently than a general-purpose computer (but are not as efficient in attacking othertypes of problems).

collection scheme for these devices, but moreoften sensors send their data straight into whatis colloquially known as the “cloud.”2 This termis commonly used to refer to anything on theother side of the customer’s wall, but in practicalterms, it means data stored and processed at thelarge centralized computing facilities known asdata centers. These facilities house thousandsof servers and provide centralized computingthat can be extremely efficient. Once informa-tion resides at a data center, it can then be com-piled, shared, and analyzed far more easily thanit could be otherwise.

Computing is now shifting toward moremobile and distributed devices and more use ofcentralized servers in data centers. That shifthas complicated the research questions aroundelectricity use and information technology.Because computers are now being embeddedin objects of all sorts, the boundary betweenwhat is computing and what is not has becomeblurred. It may in fact be impossible in practiceto determine how much electricity computingdevices use as distinct from that for otherenergy-using equipment. That is because em-bedded processors are not tracked or measuredas discrete units in many cases.

There is additional complexity brought onbecause of the broader systemic effects of morewidespread use of computing. For example,many clothes washers are equipped with sen-sors and computer technology that improve theservice being delivered and save both motorand hot water energy in amounts many timeslarger than the electricity used by the com-puting equipment. Automobile engines are al-most all computer controlled nowadays, whichgives them higher efficiency and lower emis-sions than they would otherwise have. Datacenters and the information services they de-liver enable structural changes that increase ef-ficiency throughout the economy. In each of

2This colloquial term is distinct from the concept of “cloudcomputing,” which is a way to design data centers that gener-ally results in far more efficient use of the equipment, lowerenergy use, and smaller environmental impacts per transac-tion compared with traditional “in-house” data centers.


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these cases, a relatively small amount of electric-ity used for ICT can affect much larger amountsof energy used in bigger systems.

Efficiency of Low-Energy Computing

Low-energy device consumption is governedby the schematic graph in Figure 1, which ap-plies to both computing and communicationsapplications.

The goal for low-energy devices is to min-imize the area under the power curve, so thata computing application can operate for yearsusing a primary battery or some kind of energyharvesting. Electricity use can be reduced by af-fecting the four components listed in Figure 1(active task time, active power, standby power,and transition time), as well as by reducing thefrequency of tasks (i.e., the number of times perhour the task is performed). Active power andstandby power can be reduced by improving ef-ficiency, and active task time can be reducedby faster processing (moving from an 8-bit toa 32-bit microcontroller, for example). Transi-tion time can be reduced using faster switchingtechnology. There can also be different levelsof standby—usually, the deeper the sleep, thelonger the transition time (latency) for shiftingto an active mode.

Reducing task frequency is one of themost powerful ways to reduce energy use.For example, the University of Michiganhas developed a sensor that occupies about1 cubic millimeter (10). With standby/sleeppower of 11 nanowatts (nW), a deep sleepmode of 185 picowatts, and an active power of40 microwatts (μW), it can operate for along time with a tiny battery or with energyharvesting. The developers have suggestedthat the device, if used as a tumor pressuresensor, need only communicate every 15 min.Clearly, this application does not require giga-hertz processors, and the low task frequency,combined with tiny standby losses, makes thisand many other such applications feasible.

Active power. The long-term trends in theactive power efficiency of general-purpose

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Figure 1Factors affecting energy use of computing and communication tasks.

computing devices have been well docu-mented. Koomey et al. (7) showed that theenergy efficiency of computing (measuredin computations per kilowatt-hour) doubledevery 1.6 years from the mid-1940s until 2009,a trend that is comparable to that describing ef-ficiency changes in the microprocessor era (seeFigure 2). This trend is related only to theactive power of general-purpose computersrunning at maximum computational output—itsays nothing about standby power, coding ef-ficiency, or other aspects of computer systemsthat can affect the total energy used by thesedevices. It is, however, a well-established trendaffecting an important aspect of computingtechnology, and it has been the most impor-tant driver that has made it possible for thetechnology industry to focus more strongly onlow-power devices in the past decade or two.

Will this trend continue? Nobody can sayfor certain, but we are still far from the limitsto efficiency. In 1985, the physicist RichardFeynman analyzed the electricity needed forcomputers that use electrons for switchingand estimated that there was a factor of 1011

improvement that was possible comparedto computer technology at that time (11).Performance per kilowatt-hour for computersystems improved by a factor of 4 × 104 from1985 until 2009, on the basis of regressions in


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Figure 2Energy efficiency of general-purpose computers over time (1946–2009). Adapted from Reference 7.


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Dennard scaling:a functionalrelationship betweenchip voltage andtransistor size thatallowed power use oftransistors to declinefor decades

Carbon nanotube:a form of carbon thatmay allow creation ofsignificantly fastermicroprocessors thanusing currentsemiconductortechnology permits

Koomey et al. (7), so there is still a long wayto go with current technology before reachingFeynman’s limit.

Feynman assumed a three-atom transistorto calculate his limit, but he made it clear thatif we could create smaller transistors, then thelimits would be pushed out even further. Thishas recently come to pass—Nature Nanotechnol-ogy in 2012 published an article by researchersat US, Australian, and Korean universitiesdemonstrating a reliable one-atom transistor(12). Their current version uses electron energylevels for switching. It operates only at liquidhelium temperatures, but this innovation mayultimately yield a way of capturing efficiencyimprovements beyond Feynman’s limit. Otheranalyses have pointed to physical limits wellbeyond those calculated by Feynman (13).

If the long-term trends in Koomey et al. (7)continue unabated, we will reach Feynman’soriginal limit in 2041, a little less than threedecades from now (see Figure 3). That re-sult implies that sometime during the near fu-ture our computing technology must undergofundamental changes if we are to continue ac-tive power efficiency improvements at historicalrates.

The problem is more proximate than that,however. Electrical engineering has beenwrestling with key constraints since the early2000s, and those required a fundamentalrethinking of how to improve performance andenergy efficiency of semiconductors (14, 15).In a classic paper in 1974, Robert Dennardand his colleagues (16) laid out a path forimproving performance and efficiency ofsemiconductors that held for almost threedecades (17). As transistors shrank in size,designers were able to improve efficiency byreducing the chip supply voltage (a processknown as “Dennard scaling”). Unfortunately,the limits of Dennard’s approach were reachedjust after the turn of the millennium (18).

As transistors shrank and threshold voltagelevels declined, leakage current for semicon-ductors started to increase rapidly (19), andclock speeds could no longer be increased toimprove performance. That led the industry to

move to multicore computing, packing moreand more cores on a single chip, and forcingsoftware designers to redesign their code to im-plement multiprocessing.

Multiprocessing also has limits (20), and thisrealization has forced electrical engineers toconsider other possibilities for improving pro-cessor architectures. We can still pack moretransistors on a chip, which reduces capacitanceand thus energy use per transistor, but we nolonger can count on the efficiency improve-ments that come from lower voltages, whichwere a big part of the historical gains. Thereare challenges in transistor reliability, commu-nications, and costs of fabrication that all needto be overcome if our current technologies areto continue to improve in efficiency and perfor-mance over the next two decades (14).

One promising area of research lies incarbon nanotubes, which have the potential tosubstantially improve transistor performancecompared to current technologies (21–23).IBM recently demonstrated assembly of morethan 10,000 nanotube transistors onto a singlechip (24), which is an important step towardpractical application of this technology inprocessors. Whether this technology canbe applied successfully to the production ofmicroprocessors remains to be seen.

Standby power. There are no studies of whichwe are aware documenting long-term trends instandby power over time, but the industry hasfocused on ways to improve this aspect of com-puting technology since the first laptops wereintroduced in the 1980s. In the early 1990s, theEnergy Star program for PCs first attemptedto prod desktop manufacturers to adopt lowstandby power for PCs and monitors, with sig-nificant success (25). That effort has also spreadto other kinds of ICT equipment, including faxmachines, printers, copiers, multifunction de-vices, and most consumer electronics (http://www.energystar.gov/). Improving standbypower involves both hardware and softwareinnovations and is a fundamentally differentdesign challenge than improving efficiency ofcomputers at maximum processing output.


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Figure 3When will the energy efficiency of general-purpose computers hit Feynman’s limit (11) if historical trends continue? Historical datataken from Figure 2.

Reductions in standby power are limited byleakage current and thus are related to semi-conductor architecture, voltage (26), and tran-sistor size. Since the end of Dennard scalingin the early 2000s, various methods have been

used to reduce leakage current for the fastestmicroprocessors (19), but the engineering tech-niques used for those devices are not necessarilythe same as the techniques that would be suc-cessful for ultralow-power chips running at less


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than gigahertz speeds (27). One set of promis-ing techniques revolves around the subthresh-old operation of transistors, with the potentialfor substantial improvements in efficiency (28).

Task time. Task time can be reduced byincreasing the clock speed of the computer,increasing the data bus width (a 32-bit bus cantransfer information four times as fast as an8-bit bus for a given clock speed), improvingsoftware performance, and optimizing thehardware and software to work together moreefficiently in a process known as “codesign”(29). This latter approach is common in em-bedded systems, and it is used to optimize themicrocontrollers so common in manufacturedproducts (30). It has also recently been appliedto the design of supercomputers to overcomepower-related design constraints (31–34).

Transition time. Transition time depends inpart on what kind of standby mode a devicehas entered. Typically, deeper sleep modesmean longer transition times. One of the mostcommonly used microcontrollers, the Texas In-struments MSP430, lists a typical wakeup timefrom standby of 3.5 microseconds (μs) (http://www.ti.com/msp430). For many applications,the transition time is small compared to theactive task time and can be ignored withoutsacrificing much accuracy.

Power gating, which shuts off parts of achip when they are not in use, is one methodfor reducing standby losses (35). One of thekey factors affecting transition time is the waypower gating is implemented on the chip,and different designs can affect the time tomove from standby to active power (36). Otherfactors affecting the transition time include thesystem architecture and the amount of dataor communications needed to restore activemode functionality, inherent device physics,computational speed, clever power manage-ment techniques, and the access characteristicsof memory.

Task frequency. Task frequency is in somesense arbitrary, but it is strongly dependent

on the nature of the computing task beingperformed. Low task frequency combinedwith very low standby power level allows usto apply ICTs to many new and unexpectedapplications.

Efficiency of WirelessCommunications

The long-term trends in the power efficiencyof computing also apply to some degree forcommunications, but there are additional com-plexities. The power needed to send a signalby wireless communications is governed by in-formation theory (37, 38) as well as the inversesquare law associated with electromagnetic ra-diation emanating from a source. It is also af-fected by the design of network protocols andinvolves a complex interplay between software,hardware, and user behavior (39). In many low-power applications, the transmission power andtask frequency are the most important determi-nants of battery lifetime (9, 40, 41).

Active power. There is not yet a definitiveanalysis of historical trends in active power forcommunication, but there are at least two anec-dotal data points that are suggestive. First, inAugust 2012, Kris Pister, who was the founderof one of the pioneering companies in creatingmesh networks (Dust Networks), told the firstauthor of this review (Koomey) that there hasbeen about a factor of ten reduction in activetransmission power for mesh network nodesover the past decade, which corresponds to adoubling time of about every three years foractive power efficiency.

Second, Appendix A, in the SupplementalMaterial (follow the Supplemental Materiallink from the Annual Reviews home page athttp://www.annualreviews.org), contains aschematic calculation comparing the numberof minutes of talk time for the first battery-powered cell phone to one of the simplest“feature phones” sold in 2011. This calculationshows a slower rate of improvement than thewireless mesh example (a doubling time ofalmost eight years), but that is to be expected


Supplemental Material

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http://www.ti.com/msp430

http://www.ti.com/msp430

http://www.annualreviews.org

http://www.annualreviews.org/doi/suppl/10.1146/annurev-environ-021512-110549


RF: radiofrequency

because the complex systems in cell phones areharder to optimize than simple nodes, such asthose used in mesh networks.

Both of these examples suggest that the effi-ciency of these devices has improved, althoughnot as rapidly as has efficiency for general-purpose computers. Just how far current wire-less devices are from the theoretical limits hasnot been well studied, which is why we includethis topic in the “Open Research Questions”section below. There is also a systems-level as-pect to the active power for cell phones, becausethe design of the network affects the overall ef-ficiency of data transmittal beyond the wirelesssensor or handset.

One potential area of efficiency improve-ments involves integrating antennas onto sili-con (42). Carbon nanotubes are also promisingfor improving the efficiency and performanceof radiofrequency (RF) devices. Rutherglenet al. (43), Burke et al. (44), and Hanson (45)explore different aspects of applying nano-tubes to small-scale transmitters, and Burke &Rutherglen (46) assess the whole systems designchallenge for creating transmitters at nanoscale.

Standby power. Similar uncertainty prevailson trends in standby power, with only somesystematic data compiled on this issue forwireless communications. The best existingdata are from researchers at the University ofCalifornia, Berkeley (41).

Task time. Some of the same technologicaltricks for reducing task time in general-purposecomputing can also work for communications,but information theory limits the efficiencywith which signals can be sent (9).

Transition time. Just as for general-purposecomputers, transition times between standbyand active modes are related to the characteris-tics of the relevant standby mode. Deeper sleepimplies a longer transition time. System archi-tecture also makes a difference (40).

Task frequency. For mobile sensor networks,task frequency is application specific. For mea-suring systems that change rapidly, the task fre-quency must be rapid, but for many other sys-

tems, task frequency measured in hours may beacceptable.

Task frequency for mobile phones relates towhat we might call “protocol overhead.” Mo-bile phones use protocols that have deep his-torical roots, and some of those protocols forcemobile phones to unnecessarily use power whenthey need not (for example, keeping the phoneactive when it is not really doing anything use-ful). Modest changes in how phones interactwith base stations can have substantial effectson energy use of cellular phones (reducing it bymore than 50%) (47).

Trends in Energy Storage

Energy storage comes in many different types,but for low-power electronics applications, themost important ones are primary batteries, sec-ondary batteries (also known as rechargeables),and super/ultracapacitors. Other energy stor-age devices, not discussed in detail here, in-clude fuel cells, flow batteries, and betavoltaic(nuclear) batteries. For more details, see Reddy(48) and Bogue (49, 50).3

Primary batteries. Primary batteries aresingle-use batteries that come in manyshapes and sizes. Leclanche (carbon-zinc),alkaline, and lithium are the most commonchemistries, and each of these has many vari-ants. They have improved significantly overtime (with the biggest jump attributable first toalkaline and then to lithium-based batteries),but the rate of change is nowhere near as rapidas that for computing technology. Figure 4,adapted from Reddy (48), shows that primarybatteries have improved in specific energy bya factor of about 12.5 since 1946. Over thesame period, the active power efficiency ofcomputing has increased by about 13 orders ofmagnitude (7).

Many critical applications now use lithiumthionyl chloride batteries, which have specific

3The characteristics of energy storage devices are often illus-trated using a Ragone chart, which plots power density versusenergy density on logarithmic scales (see Reference 51 for anexample).


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energies about twice that of the most commonlithium batteries shown in Figure 4 (48). Be-cause of safety concerns, these batteries are notgenerally available to consumers.

Secondary batteries. Rechargeable (sec-ondary) batteries have also improved signifi-cantly over time. Figure 5 shows a factor of∼4.7 for progress in the specific energy of such

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Figure 5Specific energy in watt-hours per kilogram (Wh/kg) of rechargeable (secondary) batteries over time, adaptedfrom Reddy (48). Abbreviations: Ni-Cd, nickel-cadmium battery; Ni-MH, nickel–metal hydride battery.


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Figure 6Specific energy of nickel–metal hydride (Ni-MH) and lithium-ion batteries inwatt-hours per kilogram (Wh/kg) after 1991.

Supercapacitor: anenergy storage devicethat uses two metalplates separated by aninsulating material,with much higherstorage capacities thana standard capacitor

batteries between 1940 and 2010 (48). Thebig jump was to lithium-ion batteries, whichare now the rechargeable battery of choicefor most electronics applications. Nickel metalhydride is still important for rechargeable AAand AAA cells used by consumers, but cellphones and other high-value electronics havealmost all switched to lithium-ion batteries.Boeing’s recent experiences with fires sparkedby lithium-ion batteries highlight some of thechallenges of this technology (52), but theenergy density advantages of these batteriesare still important in many applications.

Both lithium-ion and nickel–metal hydride(Ni-MH) batteries have improved over the pasttwo decades, as shown in Figure 6. Ni-MHbatteries improved by a factor of 2 from 1991to 2007, whereas lithium-ion technology im-proved by about a factor of 2.5 from 1991to 2010. Just as for primary batteries, thesechanges are nowhere near as rapid as for elec-tronics efficiency, but the new lithium-basedbattery technologies have been particularly im-

portant in expanding the potential for applica-tions dependent on energy storage (53).

Supercapacitors/ultracapacitors. Batteriesstore energy in chemical reactions, whereascapacitors store electricity by maintaining anelectrical potential between two conductingplates held a small distance apart. The energydensities of traditional supercapacitors (alsoknown as ultracapacitors) are given by Reddy(48) as between 5 and 12 watt-hours perkilogram (Wh/kg), which is much lower thanNi-MH or lithium-ion batteries. Superca-pacitors can be charged and discharged morerapidly than batteries, giving them advantageswhen bursts of power are needed (as forautomobile applications). They also last longerthan batteries, but the voltage they deliverfalls as they are discharged, which can be adisadvantage in certain applications.

Recent innovations using advanced materi-als show promise for increasing energy densitiesof supercapacitors. Liu et al. (54) demonstrateda graphene-based supercapacitor with a specificenergy of 85.6 Wh/kg, a level approaching thatof current-day Ni-MH batteries.

New Developments in Sensorsand Controls

The biggest shift in sensors in the past twodecades has been the development of small-scale sensors and actuators fabricated outof silicon, known in the trade as microelec-tromechanical systems, or MEMS (55). Morerecently, a class of much smaller sensors (nano-electromechanical systems) has become moreprominent (56, 57). In both cases, smaller-scalesensors allow for applications that were notpossible previously (58). One of the moreactive areas of recent research has focused onmonitoring buildings for their structural healthover time, using wireless technology (59).

Another important trend in sensors is theshift to wireless communications (9). Thereare still applications for which wired sensorsare required (mainly for high monitoring fre-quency, typically in high-reliability industrial


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production plants), but with advances in thepower efficiency and capabilities of wirelesssensors, the domain of wired sensors has begunto shrink. Wireless sensors are cheaper toinstall and offer more flexibility but at theexpense of lower bandwidth. Wireless sensorscan be battery powered, but they can also drawfrom line power, depending on the application.

Finally, the focus on low-power mobile sen-sors has highlighted the importance of optimiz-ing all parts of such systems, not just the pro-cessors (60). Analog, digital, and RF circuits allmust be redesigned to achieve long battery life-times; this represents a unique design challenge.Packaging such small devices also remains achallenge (9).

Trends in Energy Collection andTransfer Technologies

Secondary batteries and super/ultracapacitorsrequire some means to recharge them. Thetwo most important areas of research are en-ergy harvesting and intentional wireless energytransfers. The first involves tapping ambientflows of light, heat, motion, or stray RF sig-nals, and the second involves directed energytransfer without a physical connection.

Energy harvesting. One of the most dis-cussed topics related to mobile computing andcommunications is that of energy harvesting(49, 50, 61–65). There have been many inter-esting research investigations, but it has beenhard in practice to develop small-scale energyharvesting applications that are commerciallyviable. It is fair to say that energy harvesting isin its infancy.

Table 1 summarizes power densities fromdifferent ambient energy flows. Vullers et al.(66) conclude from these data that energy har-vesting “can supply approximately 10 μW to1 mW ” (p. 36), which is the sweet spot forwireless sensor nodes. Although this conclusionis true in principle, implementing it in prac-tice at a competitive cost has proved to be achallenge.

One of the few examples of energy harvest-ing in widespread use is that of tire pressure sen-sors, which have been mandated for new US au-tomobiles since 2008. These devices often relyon MEMS technology, use wireless to conveyinformation to the car’s central computer, andcan use the rotational energy of the wheel topower themselves (67–70).

Energy harvesting from ambient light is welldeveloped, especially for outdoor applications,

Table 1 Power levels for energy harvestinga

Source Source powerb Harvested powerAmbient light

Indoor 0.1 mW/cm2 10 μW/cm2

Outdoor 100 mW/cm2 10 mW/cm2

Vibration/motionHuman 0.5 m @1 Hz, 1 m/s2 @50 Hz 4 μW/cm2

Industrial 1 m @5 Hz, 10 m/s2 @1 kHz 100 μW/cm2

Thermal energyHuman 20 mW/cm2 30 μW/cm2

Industrial 100 mW/cm2 1–10 mW/cm2

RadiofrequencyCell phone 0.3 μW/cm2 0.1 μW/cm2

aReprinted from Solid-State Electronics, 53/7, Vullers et al., “Micropower energy harvesting,” pages 684–693, copyright2009, with permission from Elsevier (66).bAbbreviations: kHZ, kilohertz; mW, milliwatt; μW, microwatt.


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Nanopiezoelec-tronics: the study ofdevices that convertmotion (of clothing,for example) into smallamounts of usableelectricity

but it is harder to apply to sensor applicationsindoors because of the need for a sufficient col-lector area in areas of low light (71).

Wang (72) introduced the concept ofnanopiezoelectronics, which focuses on the useof piezoelectric materials to generate smallamounts of electricity in unusual applications.For example, researchers have demonstratedenergy capture from specially designed cloth-ing in the range of 4–16 milliwatts (mW) persquare meter of fabric (73). Other researchershave focused on tapping human body heat forpower (64).

Another interesting example is that demon-strated by Sample & Smith (74). This applica-tion harvests stray RF signals from local radioand TV broadcasts and collects enough power(about 60 μW, on average, from a TV antennaabout 4 km distant) to power sensors of differenttypes. It requires an antenna that is 33 cm longby 32 cm at its widest point, but for some ap-plications, that should not be a constraint, anddevices capable of being powered with smallerantennae are on the way.

The variability of many ambient energyflows has encouraged some researchers to de-velop hybrid power generation approaches.Bandyopadhyay & Chandrakasan (75) devel-oped an energy harvesting chip that convertsmotion, light, and heat into electricity at rel-atively high efficiency. Such a device can levelout the peaks and valleys endemic to ambientenergy and lead to more predictable rates ofenergy harvesting. Tan & Panda (71) demon-strated a different hybrid system focusing onambient light and heat.

Wireless energy transfer. In this section, wefocus on the intentional transfer of energy tospecific devices. This category of energy supplyis unique because the designer has control ofboth the transmitter and the receiver of energy.It allows for better optimization and higher-power transfers than are ordinarily possiblewith standard energy harvesting techniques.The most common application to which thistechnique now applies is in wireless-powertransfers to cell phones using commercially

available charging mats, but it has encounteredroadblocks in consumer acceptance becauseof the incompatibility between devices andthe difficulty of shifting from one standard forcharging to another (76).

Sample & Smith (74) and Sample et al. (77)describe wireless energy transfer (measured inmicrowatts) to a radio-frequency identificationdevice located no more than 10 m from theenergy source. This application uses what iscalled far-field transmission; if used in a direc-tional mode (as is commonly done with mi-crowave transmissions), the efficiency of suchtransfer can be high, but it requires line of sightto work well (78). In broadcast mode, far-fieldtechniques suffer from the dissipation of powercommon to all radiation, governed by the in-verse square law.

Another approach is demonstrated bySample et al. (79), showing coupled resonancetransfer of power measured in tens of watts,achieving about 70% efficiency at a distance of0 to 70 cm. This technique can be useful forbiomedical devices implanted inside the body(65); if sufficient energy can be transferred topower such devices, patients can avoid havinga power wire penetrating their bodies (suchwires are significant pathways for infection).

Relationships Between PowerUse, Energy Storage,and Energy Harvesting

A mobile technology becomes most feasiblewhen it can achieve either (a) long battery life-time, measured in years, or (b) tapping of am-bient energy flows so that it can operate in-definitely without changing its power source.Achieving either goal requires attention to boththe efficiency of the electronics and the charac-teristics of the chosen power source, and it isa complex optimization problem. This sectiondescribes how these factors are related.

The lifetime (L in years) of a device withaverage power PDevice

a (in watts) using a primarybattery (i.e., one that is not rechargeable) can beexpressed as the function of the actual capacityof the battery (C, in watt-hours, corrected for


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Figure 7Battery lifetime (years) as a function of power drain for some common batteries. Abbreviation:μW, microwatt.

the limitations of real batteries in use):

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where 8,766 hours is the number of hours in oneyear, adjusted for leap years. It is only when Lreaches multiple years that mobile applicationsbecome interesting, although there is no hard-and-fast rule.

Figure 7 shows the relationship betweenaverage power and battery lifetime for severalcommon battery types (Appendix B, in theSupplemental Material, contains the dataand calculation details). For power drains inthe hundreds of microwatts, lithium batteriesin the AA or C configuration are requiredto achieve lifetimes of a few years or more.Once the power drain dips below 10 μW,then even the tiny LR44 button cell battery(commonly used in watches) can achieve suchlifetimes (see Figure 8). Lithium thionylchloride batteries have roughly doubled energydensities compared to standard lithium primarybatteries, which is why they are used in manyultralow-power applications in spite of someof their limitations (48).

As an illustration, consider the sensor (dis-cussed above) developed by the University ofMichigan, which has standby power of 11 nWand active power of 40 μW. Let us assume thetask time is 2 s, the transition time is 3.5 μs(like the TI MSP430 microcontroller), andthe task frequency is 4 times per hour. Thoseassumptions combined with the formulas inAppendix B yield an average power for the de-vice of about 0.1 μW (100 nW), which would al-low the device to operate for more than a decadeusing the primary lithium version of an LR44button cell according to Figure 5 (the alka-line version might not last that long for reasonsunrelated to the power drain). Cycle times of0.5 times per hour (i.e., once every two hours)would result in an average power of 22 nW.

Let us assume that the LR44 lithium cellcould be scaled down to 1 cubic millimeter andthat the smaller cell has the same energy den-sity per liter. How long could our 22-nW loadbe powered using such a battery, which holds0.5 mWh of electricity? The answer is∼2.6 years, which is not bad for such a tiny de-vice. To achieve longer lifetimes would require



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Figure 8Battery lifetime (years) as a function of power drain for an LR44 watch battery. Abbreviation:μW, microwatt.

lower standby power and/or energy harvestingto supplement the battery.

Communication Protocols

The smart everything domain needs a com-munications infrastructure, including methodsof connecting devices together and the abilityto report and take action. Such infrastructuregenerally is built upon low-level protocols thatpackage and send information between devices.This article does not seek to list all of the tech-nology options but instead presents the rangeof the most widely used technologies. Table 2summarizes those discussed in Appendix C,which is in the Supplemental Material.

Wired communications. There are variouswired technologies, ranging from open globalnetworking standards to closed, proprietarysystems developed by specific vendors (becausethere are so many, we will not mention them).Historically, most wired communications pro-tocols in the smart domain in the residential

setting have used home power lines as a back-bone, and in commercial or industrial applica-tions, proprietary technologies have been used.The residential protocols have been for “homeautomation,” and in the business sector, theyhave been for “controls.”

Wireless communications. Even thoughwired solutions have been around for 40 years,beginning in the late 1990s the use of wirelesstechnologies in this domain has exploded.What were originally test-bed networks foruniversities and Fortune 500 businesses are nowdeployed globally in 25% of the householdswith Internet access (80) and in businesses andpublic places around the world. For wirelessnetworking, use of WiFi (IEEE 802.11 series)and Bluetooth protocols is very common.Their use in smart device and energy manage-ment domains is less common but growing. Aprimary cause of the recent upsurge in popu-larity of such applications are the smartphoneplatforms, e.g., iOS and Android, which havelowered the barrier to entry for such features



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Table 2 Overview of wired and wireless technologies for smart devices

Protocol aStandard (ifapplicable) When created Link

X10 X10 Mid-1970s Wired (PLC)UPB X10 like Late 1990s Wired (PLC)INSTEON R© — Mid-2000s Wired (PLC), RFHomePlug R© IEEE 1901 Early 2000s Wired (PLC)Ethernet IEEE 802.3 Early 1980s WiredBluetooth R© IEEE 802.15.1 Mid-1990s Wireless, 2400–2480 MHzWiFi IEEE 802.11 family Mid-1990s Wireless, 2.4 or 5 GHzWirelessHART R© IEEE 802.15.4, IEC

62591Mid-2000s Wireless, 2.4 GHz

Z-Wave — Mid-2000s Wireless, 900 MHzZigBee R© IEEE 802.15.4 Mid-2000s Wireless, ∼900 MHz or 2.4 GHz

aAbbreviations: GHz, gigahertz; IEC, International Electrotechnical Commission; IEEE, Institute of Electrical andElectronics Engineers; MHz, megahertz; PLC, programmable logic controller R©; RF, radiofrequency; UPB,universal powerline bus.

and have (again) leveraged existing infrastruc-ture (in this case, existing WiFi networks) so asto avoid the need for users to invest in or learnsignificant new technology to use these systems.

Like the wired technologies mentionedabove, there are open and closed protocols.WiFi is of course open because of its IEEEstandard roots. In the smart device domain, sev-eral are prevalent and also are generally devicefocused as opposed to being core networkingtechnologies, like WiFi.

Mesh networks. Mesh networks have untra-ditional configurations (81, 82). The nodes ofthese networks not only manage their own com-munications but also act as relays for othernodes in the network. Mesh networks are en-visioned for application areas where an other-wise pervasive communications network is notavailable, such as in a remote area where theremay be a single node with a communicationslink but all others need to “hop” their data backand forth to each other outside of the rangeof the main link. One limitation of such net-works is that they need to be built out all atonce, not piecemeal; otherwise the node densitywill not be enough to support mesh communi-cations (this factor can prevent mesh networksfrom being used in certain applications).

This type of architecture has proven popu-lar for distributed wireless networks, in part, be-cause of its power efficiency. The power neededto transmit a signal increases as the square of thedistance, so if you can send the signal over mul-tiple short hops you can reduce transmissionpower (as long as your nodes are sufficientlylow power and efficient). For transmission dis-tances greater than 10 m, transmission energybecomes the dominant term (83) in total energyconsumption.

Communication protocols for powermanagement. Nordman et al. (84) havecoined the word “nanogrid” to describe a set ofenergy producing and consuming nodes insidea computer, a sensor node, or a house. Issuesof interoperability of energy harvesting andenergy using devices in the face of time-varyingprices and other constraints will become moreprominent in a world of smart everything, butresearchers are only beginning to explore theimplications of this reality. For example, eachpart of a system needs to communicate withthe other parts, and it needs a language todo so. For sending data streams, we have thevarious protocols discussed above, but similarprotocols need to be created for managing


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power in real time for such systems to allowinteroperability of different technologies.

THE EFFECTS OFINFORMATION TECHNOLOGYON RESOURCE USE

This section discusses the four major effects ofICTs on economic activity: direct electricityuse, manufacturing energy and emissions, ef-ficiency gains from use of ICT, and increasesin consumption associated with lower costs andincreased wealth. As the technology shifts to-ward ultralow-power mobile devices, the directelectricity use of these devices will likely declinein importance, whereas the emissions associatedwith manufacturing will claim a larger share ofthe direct emissions associated with ICT (85).The indirect effects of ICT will also loom largerand will likely overshadow the direct environ-mental impacts of ICT.

Use-Phase Electricity Consumptionof Information and CommunicationTechnology Equipment

The traditional categories of ICT equipmentare data centers, networking equipment,telephone network equipment, on-premisecomputing/office equipment, and embeddedsystems, but there are always difficult boundaryissues to address in estimating use-phase elec-tricity use. For example, data centers, commer-cial buildings, and industrial plants all containnetworking equipment, so studies that attemptto estimate networking equipment electricityuse need to differentiate between equipmentin data centers and those in other places.Telecommunications networks were formerlydistinct from Internet networks, but as old-styletelecom equipment has been replaced by newervoice-over-IP (Internet protocol) equipment,that distinction has faded. And the electricityused by embedded sensors and computing con-trols in appliances and industrial applicationsis almost impossible in practice to estimate

because the needed data are closely held pro-prietary secrets of many different companies.

With the exception of data centers andnetworking equipment, there has been littlecredible, transparent, reproducible, peer-reviewed work conducted on total ICT poweruse since the early 2000s. Around that timethere was controversy over how much ICTcontributed to US power demand, with manyanalysts uncritically accepting erroneous butwidely circulated claims that ICT used 13% ofUS electricity and would grow to 50% over thenext decade (86–88). Out of that controversygrew peer-reviewed reports and articles onUS ICT electricity use (89–91) that showeddefinitively that the US total was about 3%,not 13%. It also led to the first peer-reviewedmeasurements of electricity used in a datacenter (92) as well as to follow-on research(93).

The most comprehensive attempt to com-pile ICT electricity use since 2002 was con-ducted by Malmodin et al. (94). It assessed ICTelectricity use and carbon emissions, includingboth the manufacturing of equipment as well asthe use phase, focusing on 2007. Such analysesalways contain assumptions and simplifications,but this study attempted to be as comprehensiveas possible. It arrived at a global total of electric-ity used for ICT in 2007 of 930 terawatt-hours(TWh), or about 5.4% of global electricityconsumption in that year (17,110 TWh, ac-cording to Reference 95, assuming 7% averageline losses). This total includes telephonenetworks, mobile phone networks, the Internetcore network, data centers, and some end-userequipment (PCs, fax machines, cell phones,etc.). It is not entirely clear whether all types ofoffice equipment (copiers and business print-ers) are included in this total. In addition, manythings have changed since 2007, so it is notknown whether the percentage of all electricityconsumption has increased or decreased sincethen.

More recent work is sparse. Lanziseraet al. (96) analyzed global and US networkingelectricity use for 2007 and 2008 and projected


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LCA: life-cycleassessment

EIO: economicinput-output

it to 2012. They found total electricity usedfor global networking was about 58 TWh,representing about 0.3% of the total electricityconsumption in 2010 (18,118 TWh). If coolingand other auxiliary energy uses are included,the total would be a little over 100 TWh, or0.6% of 2010 consumption. The numbers arehigher in percentage terms for the UnitedStates, with US networking totaling about0.5% in 2010 (0.9% with infrastructure energyuse).

Lambert et al. (97) built on the work ofLanzisera et al. and did the most comprehen-sive available review of various studies on net-working electricity use. Lambert et al. also dealtwith some (but not all) of the boundary is-sues associated with estimating total electricityused by communications equipment (includingtelecommunication networks).

For all data centers, Koomey (98), build-ing upon his earlier work (99), estimated to-tal global electricity use for 2010 of between203 and 272 TWh (which includes cooling andother infrastructure energy use), or between1.1% and 1.5% of the total. For the UnitedStates in 2010, data center consumption wasbetween 1.7% and 2.2% of total US electric-ity consumption.

There is overlap between the Koomey andthe Lanzisera data sets, with some network-ing equipment housed in data centers. No onehas yet attempted to reconcile the two sourcescompletely, but there is clearly some dou-ble counting—the percentages cannot just beadded together.

The shift toward mobile devices has pro-found implications for the total direct electric-ity used by ICT equipment. Because these de-vices are battery powered, they use orders ofmagnitude less power than devices that drawelectricity from power lines. They also exist innumbers much greater than conventional com-puters, but it is not yet clear whether greaternumbers will offset higher efficiencies and causethe total electricity use associated with ICT toincrease. Assessing this issue will require care-ful analysis for the world akin to that conducted

in the early 2000s for the United States, witha special focus on distributed computing ap-plications and careful attention to boundaryissues.

Manufacturing Energy and EmissionsAssociated with Information andCommunication TechnologyEquipment

As computing shifts from general-purposecomputers to low-power devices, there are twoeffects on direct greenhouse gas emissions.First, the use-phase energy consumption perdevice goes down significantly compared toICTs that draw electricity from power lines;the most extreme example is that of datacenters, where the use-phase still dominates(100). Second, the mass of these devices goesdown compared to general-purpose comput-ers, which reduces manufacturing energy.In general, the use-phase energy falls fasterthan the mass, yielding an increase in thecontribution of manufacturing and productdelivery to total greenhouse gas emissions. Tounderstand these changes, we rely on life-cycleassessments (LCAs).

How is life-cycle assessment done? Thelife-cycle inventory part of LCA uses process,economic input-output (EIO), and hybridmethods. We use the term process to denotethe most common form of life-cycle inventory,delineated by the International StandardsOrganization (101). This method is based ona bottom-up model of a supply chain, witheach constituent process described in terms ofmaterial inputs and environmentally significantreleases or outputs. The method to compile theinventory ranges from the simple constituentsumming of a supply chain to a matrix formula-tion that holistically accounts for circularity ef-fects (102–104). EIO-LCA, by contrast, utilizestop-down macroeconomic models that describea national economy via monetary transactionsbetween sectors (105–107). Combined withsector-level environmental data, these models


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can be used to estimate the total supply-chainimpacts of production, though at a moreaggregated level than process models. HybridLCA is an umbrella term for approaches tocombine both process and EIO methods inways that address their respective weaknesses(108–110).

Life-cycle assessment and electronics. Theapplication of LCA to electronics started in the1990s (111) and has since evolved to becomea critical part of the debate on environmentalinterventions. LCA studies of electronics havedemonstrated the importance of high-purityprocessing in the supply chain (112, 113),shown that manufacturing of PCs can consumemore energy than their operation (114, 115),and revealed that lead-free solder alternativesare far from impact free (116). When the man-ufacturing energy exceeds that of operation,extension of the product’s life span can be aneffective option for mitigating energy use andother impacts. This idea led to the inclusion ofreuse criteria in the environmental certificationof computers (see, e.g., the EPEAT R© ratingsystem, http://www.epeat.net).

Electronics are particularly challenging forLCAs for a number of reasons. One challengeis a complex supply chain involving high-techprocesses for which material flow data are dif-ficult to collect. A second challenge is the rapidevolution of processes (117) and product func-tionality (118), as well as how products are usedby consumers (119). A third challenge is thefar-flung international supply chain for manu-facturing and recycling electronics.

These challenges and a lack of consensus inthe LCA community have led to divergent LCAresults. For example, a process LCA analysis oflaptop computers, contracted by the EuropeanCommunity to inform the Energy UsingProducts Directive, reported 27% and 73%energy shares for production and operation,respectively, whereas a hybrid LCA analysisobtained roughly the opposite: a 62–70% sharefor production and 30–38% for operation(115). Some argue for the importance of EIOor hybrid techniques to ensure complete cover-

age of the supply chain (114, 120–122). Otheranalysts prefer the process-sum approach (123),some arguing that EIO-LCA estimations ofcomponent contributions are inaccurate (124).The pivotal philosophical issue in the choice ofmethod is: When facility-level information fora process is unavailable, is it preferable to use anuncertain estimate (EIO-LCA) or to eliminatethat process from the supply chain (processsum)? A consensus in the LCA community onthis question is still being developed.

Trends in scale and production versus op-eration share. Although LCA is still evolving,it is still worth looking for general trends per-tinent to the hardware evolution considered inthis article. We focus on the magnitude of en-ergy use and the breakdown between manufac-turing and operation for different types of de-vices. The production versus operation share isimportant for two reasons. First, a larger sharefor production implies additional complexityand uncertainty in the assessment owing to theneed to model material flows in supply chains.Second, the manufacturing versus operationshare may suggest priorities for interventions.

There are a number of drivers affecting life-cycle energy use. One is technological progress.On one hand, technological progress reducesimpacts per functionality (e.g., energy per tran-sistor) for both manufacturing (118) and oper-ation (7), though not necessarily at the samerate. On the other hand, increasing function-ality embedded in succeeding generations ofdevices tends to increase impacts (118). An-other factor is the type of functionality deliv-ered by the device. Higher computational load(e.g., servers) and graphic output (e.g., monitorsize/resolution) tend to increase operational en-ergy use. Smaller device size reduces energy usein producing constitutive materials.

Figure 9 shows the carbon dioxide (CO2)emissions for production and operation of aserver, laptop, smartphone, and flash memorychip (125, 126). The progression of products,from server, laptop, and smartphone to flashmemory chip illustrates trends toward thedecreasing intensity of computation and


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0% 20% 40% 60% 80% 100%

Server (Mac Mini OS X server)

Laptop computer (Macbook Pro 13")

Smartphone (iPhone 5)

NAND flash memory - 1 GB

Share of CO2 emissions

Production Operation

0 200 400 600 800 1,000 1,200

Life-cycle CO2 emissions (kg)

Figure 9Life-cycle CO2 emissions for server, laptop, smartphone, and flash memory chip. Sources: Reference 125 for server, laptop, andsmartphone, and Reference 126 for the memory chip.

decreasing physical size. The results indi-cate decreasing life-cycle CO2 emissions asa function of these two trends and a shiftfrom the operation phase being dominant tomanufacturing becoming more important as asource of emissions.

Given the uncertainty in LCA mentionedabove, how robust is this trend? Although a fullanalysis is beyond the scope of this review, otherstudies provide supporting evidence. A process-sum analysis of a rack server yielded a pro-duction/operation split of 5% production and95% operation (127). The hybrid laptop analy-sis, mentioned above, was 60% CO2 emissionsfor production and 40% for operation (115),and an analysis of smartphones reported a 76%production and 24% operation split (128).

What do these results indicate for the di-rect environmental impacts of the cloud of smallsensors and other devices discussed in this ar-ticle? First, the manufacturing phase is likelyto be a critical factor, implying that an LCAis required to properly assess the magnitude ofimpacts. Second, the magnitude per device willprobably be small, suggesting that the numberof devices will drive impacts.

Information and CommunicationTechnology and Economic Efficiency

When economists talk about efficiency, they of-ten refer to a concept known as the “produc-

tion possibility frontier,” which expresses thelevel of output possible for the economy givencurrent technology and as a function of howoutput is distributed between two representa-tive goods (129). If an economy is operating onthe frontier, it has achieved maximal economicefficiency. As Sanstad et al. (130) put it,

along this frontier there is a resource con-straint, so that increasing one output requiresreducing some other output. This resourceconstraint that prevails at the frontier is thesource of opportunity cost, the loss in outputof one type when another type of output isproduced instead. When goods are appropri-ately priced, an optimal or efficient allocationexists. (p. 1300)

Real economies often diverge from the sim-plified models in economic textbooks, and sothey operate at less than optimal efficiency(131). Sometimes new technologies (like ICT)can help firms and consumers move closer tothe production frontier because they make mar-kets work more like the idealized economicmodels. Technologies, such as ICT, can re-duce information costs, thus moving consumersand firms closer to having perfect information(a necessary condition for optimal efficiency).For example, Jensen (132) showed significanteconomic efficiency improvements from the


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introduction of mobile phones for localfisheries in south India. Another effect of tech-nology may be to expand the frontier, so that theresource constraints that were binding in thefirst case become less so as ICT reveals or cre-ates opportunities that were not there before.

Capabilities Enabled by Informationand Communication Technology

ICT speeds up our ability to collect data,manage complexity, and more rapidly learnand adapt, with the potential to alter the waythe economy operates. A list of new capabilitiesenabled by these technologies is contained inReferences 87 and 133 and repeated below.

1. Near-zero marginal cost of reproductionand distribution

2. Quicker publishing3. Easier sharing of data4. Quicker review of technical material5. Easier ordering and distribution6. Direct feedback from suppliers to con-

sumers (and vice versa)7. Indirect feedback from consumers to sup-

pliers (through data collection)8. Collaboration among users9. Access to information 24 hours per day

10. Universal searching11. Easier and more widespread public access

to technical information12. Dematerialization of products and

services13. Improving measurement and verification

of processes14. Improving the speed and accuracy of

analysis15. Enabling more rapid institutional change

The first 11 points of this list (87) relateto dissemination of existing information, rep-resent improvements in the way the economyfunctions, and are likely to have measurablebut incremental effects on economic activity.In contrast, the last four ideas apply ICT toinnovations (what economists refer to as in-vention) for both technologies and institutions;these factors will probably result in large struc-

tural changes in the economy and thus have par-ticular importance to our narrative. This dis-tinction between the two parts of this list paral-lels the discussion of economic efficiency above,where the information dissemination technolo-gies move the economy closer to the produc-tion frontier, and technologies generating newinnovation expand that frontier so that new op-portunities become manifest.

Because of the importance of invention totransforming economic activity, we explore thecomponents of the second group of capabilitiesin more detail below.

Dematerialization of products and services.It is not always true that using bits instead ofatoms reduces emissions, but it is often true.Hilty et al. (134), building on concepts takenfrom UNEP (135), define dematerialization as“resource decoupling,” which separates eco-nomic growth from growth in the use of ma-terials and resources. It is usually possible tomake products simpler in design using softwareand controls in the device itself, but we can alsosave energy and materials by avoiding the needto move physical objects and people from placeto place. The three archetypal examples of thiseffect are telecommuting (136), replacement ofphysical compact discs with downloadable mu-sic (137), and video conferencing (138).

Improving measurement and verificationof processes. Because of the rapid decline inthe costs of monitoring technology (driven byimprovements in computing and communi-cations), our ability to understand the effectsof our actions in real time is increasing at afurious pace. This means better control ofprocesses, less waste, and better matching ofenergy services demanded with those supplied.The most sophisticated data center operators,for example, have sensors that measure tem-perature, humidity, power flows, and otherkey information tens or hundreds of times persecond, so their control systems will not missanything. In general, the more accurate themeasurements, the easier it is for economic


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actors to respond to reality appropriately andthe better markets will function (139).

Improving the speed and accuracy ofanalysis. Fortunately, the inrush of data frommonitoring technologies has been accompaniedby improvements in our ability to analyze andunderstand those data. Without new tools wewould have a hard time keeping up with the in-formation, which is why new data centers andindustrial operations are increasingly demand-ing more powerful tracking software.

These developments are important becausethe information that is starting to become avail-able on energy use will be at increasingly finelevels of geographic and temporal disaggrega-tion. With the proliferation of “smart meters”that allow real-time metering of electricity use,our ability to understand electricity use in build-ings will rapidly improve (140). For example,in the early days of energy efficiency analy-sis (in the 1970s), we conducted market as-sessments using simple averages of costs andsavings for a single refrigerator model for theUnited States as a whole (141). Soon we willbe able to monitor the response of millionsof households to electricity price in real timeand to disaggregate household electricity intoits component parts with unparalleled accuracy.That will allow much more precise assessmentsof efficiency potentials and will give businessesthe opportunity to target the biggest electricityusers with energy-saving innovations.

Enabling more rapid institutional change.When companies first started buying comput-ers on a large scale, economists were puzzledby the apparent lack of effect on produc-tivity (this puzzle eventually became knownas “the productivity paradox,” as describedin Reference 142). This delay actually hadhistorical precedent. With electric motors, forexample, the real benefits of that technologydid not arrive until production processes weremodified to take full advantage of the newtechnology’s benefits, and the same was truefor computers (143). Once companies reor-ganized themselves to capture those benefits,

productivity improvements started on a rapidupward march that continues today (2).

But it is not just that ICT requires thatcompanies reorganize themselves to take fulladvantage of its benefits, it also makes suchreorganization easier because it improvescommunication, coordination, and processcontrols, and it creates the conditions underwhich complementary cost-reducing innova-tions can more rapidly be brought to market(143). It is in this deep sense that ICT is atransformational technology.

Information and CommunicationTechnology and Energy Consumptionin the Whole Economy

With the advent of “the Internet of things,”ICTs offer the prospect of unprecedentedvisibility to the flows of energy, emissions,materials, and dollars throughout the economy,which promotes increased efficiency (bettertracking and management of these flows willallow consumers and businesses to optimizethem in ways they never could before). Theyalso make it easier and faster to restructureinstitutions, thus allowing efficiency improve-ments to spread more rapidly throughout theeconomy. Because total use-phase electricityconsumption by ICT is relatively small com-pared to that of the broader economy (probablyrepresenting less than 10% of the total), andbecause ICT can have systemic effects on theoptimization of non-ICT energy use in theeconomy, it is in our opinion likely that the neteffect of ICT has been and will continue to beto increase overall energy efficiency, but thisproposition is difficult to prove conclusively.

Estimating use-phase ICT electricity con-sumption is the easiest part of the problem, butit is still not trivial. Much of the data for as-sessing use-phase consumption are proprietary,so accurate calculations are difficult, particu-larly for data centers and telecommunicationsystems. For low-power embedded systems, theproprietary problem is overlaid on top of poortracking of components because these systemsare usually a tiny part of a much larger system or


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device. The technical knowledge to do a carefuljob on this task is also rarely found, as it requiresintegrating many different technical sources ina way that illuminates rather than obscures theuncertainties in the data and calculations.

An even more complex issue is that the ef-fects of ICT are often systemic and cannotbe easily attributed to individual energy-usingdevices. This makes assigning causality prob-lematic. For example, the advent of electronicsystems for dynamically setting airplane ticketprices and related systems for consumers to dis-cover them has presumably led to increases inair travel, but these changes have also had othersystemic effects on airline operations that aredifficult to disentangle.

ICT has probably led to increasing load fac-tors on planes that would have flown anyway(which increases energy use somewhat), butto the extent that more planes are flying thanwould have otherwise, that increase in energyuse in principle should also be counted. Suchenergy penalties must also be weighed againstenergy savings (and other cost savings) thatmight accrue from better data collection, im-proved measurements, and superior optimiza-tion of airline systems. Airlines now organizethemselves differently than they used to, inlarge part because of the advantages of ICT,so those effects are also germane. The com-plexity of such assessments is daunting, evenfor employees of airline companies. There issimply no prospect for accurate calculations ofthese energy-related effects for the economymore generally. That is why carefully craftedcase studies are so important.

Information and CommunicationTechnology and Broader Measuresof Consumption

Because ICT increases efficiency and reducescosts, it can lead to greater wealth and thus pro-mote greater consumption in certain instances.There is little credible quantitative research onthis issue, and it is notoriously difficult to sepa-rate the various interrelated effects.

One of the key complexities in assessing theeffect of ICT on consumption is that the struc-tural innovations enabled by this technologymake it almost impossible to model the relevanteffects. ICT allows us to modify institutionalarrangements, so economic models estimatedfrom historical data can tell us little about whatthese structural changes might imply (133).

This effect of technology on wealth and con-sumption is well known in economics, but it isoften confused by naive observers with “the re-bound effect” [see, for example, Owen (144)]. Ifpeople become richer over time and want big-ger houses and cars, that is not the reboundeffect. The causes of increased wealth are manyand varied, and efficiency is only one of manyfactors contributing to this trend. It is only thechange in behavior that can be assigned di-rectly to improved efficiency that can properlybe called the rebound effect, and in general,those effects are modest. The only case relatedto ICT where it might be significant is in datacenters, but we have been able to identify onlyone data center where peer-reviewed empiricaldata indicated the existence of a sizable reboundeffect (92, 93).

OPEN RESEARCH QUESTIONS

The new capabilities enabled by informationtechnology will create new challenges. Thissection summarizes some research questionsthat emerge from the review above, to help setthe agenda for further study.

Direct Electricity Used by Informationand Communication Technologies

The best current knowledge indicates that elec-tricity used by ICT is probably less than 10% oftotal electricity consumption, but there is sig-nificant uncertainty around that number. Thelatest attempt at such an analysis focused onworldwide ICT consumption in 2007 (94), andno one to our knowledge has compared thoseresults in a consistent way to the earlier analy-ses (86, 89, 90) or to related analyses focusing


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on specific ICT segments for more recent years(96–99).

The effort to tally direct electricity use re-quires a significant effort by a single researchgroup (the complexity of the task and the needfor consistency across different ICT segmentsmake such integration imperative). It is likelythat the shift to ultralow-power devices willmake the total direct electricity use a less crit-ical issue in the future, but there is also a shifttoward centralized computing (in data centers),and so those facilities will still need to be trackedwith care.

Effect of Informationand Communication Technologyon the Broader Economy

The difficulties in analyzing the effects of ICTson the broader economy argue for the creationof carefully designed case studies rather thanhigh-level modeling. Except in rare circum-stances, economic models are unable to assesssituations where the underlying structural rela-tionships in the economy are changing rapidly,but this kind of change is exactly what ICT en-ables. Instead, the focus should be on develop-ing before-and-after case studies to documentthe effects of ICT on production processes infirms or on behavior within a well-defined cus-tomer segment.

Quantifying Network Electricityand Emissions Associatedwith Data Transfers

As more opportunities arise for dematerializa-tion, the field will need more accurate esti-mates of the electricity intensity of networkdata flows. For example, in their comparisonof downloading music to buying physical CDs,Weber et al. (137) used an upper bound esti-mate of the electricity intensity of data flows(145) and showed significant emissions savingsfor downloads even in that case. Other applica-tions will require use of “best estimate” num-bers for data flow electricity intensity, and this

will require accounting for the rapid changes innetwork data flows and equipment over time,for average intensities versus marginal intensi-ties, and for the serious boundary issues thataffect most such comparisons. None of theseissues have been well characterized so far, al-though there is movement toward more com-prehensive treatment of the electricity intensityof data flows (146, 147).

Possible Rebound Effectsin Data Centers

The one area of ICT where rebound ef-fects could be important is data centers. Manyof these facilities are power or cooling con-strained, which means that improving the ef-ficiency of computing equipment will free upinfrastructure to power more servers. For busi-nesses constrained in this way, ICT efficiencymight lead to the installation of more ICTequipment, with the total facility energy usestaying about the same. The only data centerfor which this effect has been documented inthe peer-reviewed literature is in California (92,93), and the efficiency improvements in the fa-cility allowed it to expand the ICT equipmentfootprint. It is not known what fraction of datacenters face such power constraints.

Comparison of Current Wireless DataTransmission Efficiencies toPhysical Limits

Anecdotal data show that there have been sig-nificant improvements in the efficiency of wire-less data transmission over the past few decades,but no one has yet published comprehensivework in that area. Once such data exist, it willbe imperative to compare current and histori-cal data transfer efficiencies to the physical lim-its, in the same way as the text above com-pared actual active power computing efficiencyto Feynman’s limit. Such a comparison will re-veal just how much more potential there is forimproving transmission efficiencies. Cook et al.(9) summarize important foundational work


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related to such a comparison, but more workis clearly needed in this area.

Achieving Even Lower-Power Levelsfor Information and CommunicationTechnologies

Some of the biggest technical challenges forICTs will be in reducing power use, especiallyin standby mode. We have made great strides inthis area in recent years, but achieving anotherone to two orders of magnitude improvementin “best-in-class” standby power (reaching pi-cowatt levels) would have a dramatic effect onthe range of mobile applications that becomefeasible. Improving data transfer efficiencies byan order of magnitude would not hurt either,and both of these goals are within reach in thenext decade or so.

Identifying New Applications forLow-Power Information andCommunication Technologies

Successfully integrating ultralow-power ICTsinto new products will require research intowhere small amounts of real-time informationcan have the highest value. Such research willoccur at universities, but there will also besignificant experimentation in entrepreneurialstart-up companies because the potential fordisruptive innovations is so high in this designspace.

Creating New Analytical Tools toCope with the Inrush of New Data

One of the biggest buzzwords in technology in-dustry today is “big data,” but as mobile sen-sors become ubiquitous, our sense of what rep-resents big data will change rapidly. We willneed to develop new analytical tools and tech-niques to face this inrush of data, and we willgrow to rely more on sophisticated softwareand machine-learning techniques to help peo-ple make sense of it all. We will also becomemore selective about which data we collect and

will use context to determine which to keep andwhich to ignore.

CONCLUSIONS

Small amounts of information can have im-mense value, particularly for systems for whichwe have little real-time data (which is trueof most systems in modern economies). Theadvent of ultralow-power ICTs portends bigchanges in the way we understand and respondto the world around us because they allow real-time visibility into flows of energy, materials,and dollars. That visibility allows us to identifyopportunities for cost reductions more effec-tively than we ever could before.

Dramatic improvements in power require-ments, costs, and wireless data transfers havepropelled us into a new design space for ICT.The primary factor enabling this developmentis the long-term trend toward high energyefficiency for computing, which resulted in 13orders of magnitude improvement in the activepower efficiency of computing since 1946.Improvements in computer standby power,communications, energy storage, energyharvesting, directed wireless energy transfers,and sensors have also contributed to makingthese new devices possible, but it is the changein active power efficiency, because of its mag-nitude, that is most responsible for the recenttechnological focus on low-power informationsystems.

The direct effects of manufacturing and us-ing ICTs have been of primary environmentalconcern in the past, and those effects will con-tinue to be tracked, but the shift to ultralow-power devices will focus more attention on thepotential effects of these technologies on therest of the economy. Computing and communi-cations can be embedded in virtually any object,which means we will be able to optimize mostsystems and reduce costs significantly. The im-plication of these developments for the futureis profound. These new technologies can resultin a more flexible and efficient world than hasever existed, if we use them wisely. Here’s hop-ing that we do.


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DISCLOSURE STATEMENT

The authors are not aware of any affiliations, memberships, funding, or financial holdings thatmight be perceived as affecting the objectivity of this review.

ACKNOWLEDGMENTS

In the course of our writing this review, many friends and colleagues aided our research, and forthis they have our gratitude. We list them in alphabetical order by company: Nicholas Cizek,Advanced Research Projects Agency-Energy (ARPA-E); Dominic Pajak, David Flynn, Erik Ploof,Ian Ferguson, and David Pietromonaco, Advanced RISC Machines (ARM); Zachary Schmidt,Analytics Press; Sam Bose, AquaMW; Gene Silverman, Berkeley Springs; Kris Pister, DustNetworks (and University of California, Berkeley); Michael Sesko, Encendia Biochar; Sam Cohenand Chris Burmester, Energy Solutions; Eduardo Montanez, Freescale Semiconductor; BernardAboussouan, GainSpan; Veeru Ramaswamy, General Electric (San Ramon); Luiz Andre Barroso,Google; Lorie Wigle, Andy Keates, Winston Saunders, and Henry Wong, Intel Corporation;Fred Ziari, IRZ Consulting; Alan Meier, Bruce Nordman, and Richard E. Brown, LawrenceBerkeley National Laboratory; Erik Brynjolfsson, Hari Balikrishnan, Matt Beane, Tod Loofbour-row, Jekanthan Thangavelautham, Jin Joo Lee, and Rahul Sarpeshkar, Massachusetts Instituteof Technology; Stephen Vitale, Peter Grossman, and Peter Wyatt, Massachusetts Institute ofTechnology Lincoln Laboratory; Rob Cosaro, NXP; David O’Reilly, Proteus Biomedical; CarlWerner, Rambus; Jonathan Chizick, Samsung; Rob Conant, Trilliant; David Culler, Jan Rabaey,Sam Borgeson, and Branko Kerkez, University of California, Berkeley; David Blaauw, Universityof Michigan; Josh Smith and Luis Ceze, University of Washington; and Luke D’Arcy, Whitespace.

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http://energy.lbl.gov/EA/emills/commentary/docs/carbonemissions.pdf

http://energy.lbl.gov/EA/emills/commentary/docs/carbonemissions.pdf

EG38-FrontMatter ARI 15 September 2013 14:5

Annual Review ofEnvironmentand Resources

Volume 38, 2013

ContentsIntroduction � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �v

Who Should Read This Journal? � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �vii

I. Earth’s Life Support Systems

Environmental Tipping PointsTimothy M. Lenton � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 1

Regional and Global Emissions of Air Pollutants: Recent Trendsand Future ScenariosMarkus Amann, Zbigniew Klimont, and Fabian Wagner � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �31

Pyrogeography and the Global Quest for Sustainable Fire ManagementDavid M.J.S. Bowman, Jessica A. O’Brien, and Johann G. Goldammer � � � � � � � � � � � � � � � � �57

II. Human Use of Environment and Resources

A Global Assessment of Manufacturing: Economic Development,Energy Use, Carbon Emissions, and the Potential for EnergyEfficiency and Materials RecyclingTimothy G. Gutowski, Julian M. Allwood, Christoph Herrmann, and Sahil Sahni � � � � �81

Life-Cycle Assessment of Electric Power SystemsEric Masanet, Yuan Chang, Anand R. Gopal, Peter Larsen, William R. Morrow III,

Roger Sathre, Arman Shehabi, and Pei Zhai � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 107

Methods and Models for Costing Carbon MitigationJayant Sathaye and P.R. Shukla � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 137

On the Sustainability of Renewable Energy SourcesOttmar Edenhofer, Kristin Seyboth, Felix Creutzig, and Steffen Schlomer � � � � � � � � � � � � � 169

Smart GridsPeter Palensky and Friederich Kupzog � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 201

Water Conservation: Theory and Evidence in Urban Areas of theDeveloped WorldDavid Saurı � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 227

Agricultural Biotechnology: Economics, Environment, Ethics,and the FutureAlan B. Bennett, Cecilia Chi-Ham, Geoffrey Barrows, Steven Sexton,

and David Zilberman � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 249

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Recent Advances in Sustainable Buildings: Review of the Energy andCost Performance of the State-of-the-Art Best Practices fromAround the WorldL.D. Danny Harvey � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 281

Smart Everything: Will Intelligent Systems Reduce Resource Use?Jonathan G. Koomey, H. Scott Matthews, and Eric Williams � � � � � � � � � � � � � � � � � � � � � � � � � � � 311

State of the World’s Nonfuel Mineral Resources: Supply, Demand,and Socio-Institutional FundamentalsMary M. Poulton, Sverker C. Jagers, Stefan Linde, Dirk Van Zyl,

Luke J. Danielson, and Simon Matti � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 345

Global Environmental Change and Human SecurityKaren O’Brien and Jon Barnett � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 373

III. Management, Guidance, and Governance of Resources and Environment

Actionable Knowledge for Environmental Decision Making:Broadening the Usability of Climate ScienceChristine J. Kirchhoff, Maria Carmen Lemos, and Suraje Dessai � � � � � � � � � � � � � � � � � � � � � � � 393

Market Instruments for the Sustainability TransitionEdward A. Parson and Eric L. Kravitz � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 415

Methods and Global Environmental GovernanceKate O’Neill, Erika Weinthal, Kimberly R. Marion Suiseeya, Steven Bernstein,

Avery Cohn, Michael W. Stone, and Benjamin Cashore � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 441

Humans and Nature: How Knowing and Experiencing Nature AffectWell-BeingRoly Russell, Anne D. Guerry, Patricia Balvanera, Rachelle K. Gould, Xavier Basurto,

Kai M.A. Chan, Sarah Klain, Jordan Levine, and Jordan Tam � � � � � � � � � � � � � � � � � � � � � � 473

IV. Integrative Themes

Preindustrial Human Impacts on Global and Regional EnvironmentChristopher E. Doughty � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 503

IndexesCumulative Index of Contributing Authors, Volumes 29–38 � � � � � � � � � � � � � � � � � � � � � � � � � � � 529

Cumulative Index of Article Titles, Volumes 29–38 � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 533

Errata

An online log of corrections to Annual Review of Environment and Resources articles maybe found at http://environ.annualreviews.org

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