An ad blocker will reduce your carbon footprint
Installing an ad blocker can significantly reduce your web data use
and internet carbon footprint. I’m interested to know your thoughts on
that, as it’s slightly controversial. Before getting to the
ad-blocker, here’s a reminder of the key thing that you can do to reduce
your internet carbon footprint: limit streaming 4K video on large TVs.
That was the conclusion of my last post, where I took a look at the carbon cost of internet data and estimated that:
- Data use in 2022 will average around 3,650 GB per person in the US, equating to an annual carbon footprint of 200 kg CO2 each.
- Watching video is responsible for the lion’s share of most people’s data footprint.
- Streaming 4K video to a large TV uses around 10 times more data (up to 7 GB per hour) than SD video, and 28 times more data than viewing video on a phone.
- So, a heavier user who streams 5 hours of 4K video per day will have a fairly significant data carbon footprint of 700 kg CO2, annually. That’s about the same as driving 3500 km (2200 miles).
So, the best way for most to reduce their internet carbon footprint
is to manage without a huge 4K television, or at least be frugal with
streaming ultra-high definition (UHD) video on it. Bear in mind that the
information technology and communications (ICT) sector now accounts for
around 3.7% of global CO2 emissions, more than air travel.
But there is another thing that you can think about: Using an ad
blocker on your web browser to reduce your data use and hence your
internet carbon footprint.
A study from Simon Fraser University
showed that using AdBlock Plus reduced data use by 25-40%. Data use for
a general web-browsing session was reduced by 25%, while data use for
video-rich sessions was reduced by 40%. You can find a list of
recommended ad blocker plugins for Mozilla Firefox here.
Is there a downside? Well, many websites and content creators do rely
on advertising revenue to pay the bills. And I’ll emphasize again that
if you’re a fairly light internet user then your data carbon footprint
is not that significant. By far and away the biggest culprit is
streaming UHD (4K or 8K) video on large TV screens.
Besides limiting (4K) video streaming, one of the easiest and most effective ways to reduce your online carbon footprint is to use Ecosia as your primary search engine.
Source: greenstarsproject.org
Energy Conservation with Open Source Ad Blockers
1 Department of Electrical & Computer
Engineering and Department of Materials Science & Engineering,
Michigan Technological University, Houghton, MI 49931, USA
2 Department of Electronics and Nanoengineering, School of Electrical Engineering, Aalto University, FI-00076 Espoo, Finland
Abstract
Internet-related electricity consumption is rising
rapidly as global Internet users spend more than 6.5 h per day online.
Open source ad blockers have the potential to reduce the time and thus
electricity spent using computers by eliminating ads during Internet
browsing and video streaming. In this study, three open source ad
blockers are tested against a no-ad blocker control. Page load time is
recorded for browsing a representative selection of the globally
most-accessed websites, and the time spent watching ads on videos is
quantified for both trending and non-trending content. The results show
that page load time dropped 11% with AdBlock+, 22.2% with Privacy
Badger, and 28.5% with uBlock Origin. Thus, uBlock Origin has the
potential to save the average global Internet user more than 100 h
annually. The energy conserved if everyone in the United States used the
open source ad blocker would save over 36 Americans lives per year if
it were to offset coal-fired electricity generated-based pollution. In
the United States, if all Internet users enabled Privacy Badger on their
computers, Americans would save more than $91 million annually. Globally, uBlock Origin could save consumers more than $1.8 billion/year. Open source ad blockers are a potentially effective technology for energy conservation.
1. Introduction
Americans now spend unprecedented amounts of time online. The
Surveying the Digital Future
study found that 92% of Americans are Internet users and that, on
average, since 2000, the time they spent online more than doubled from
9.4 h to 23.6 h per week [
1]. A lot of this Internet time is at home, which has risen more than fivefold from 3.3 to 17.6 h per week [
1]. With this rapid growth in Internet use, concern has arisen over the resultant electricity consumption [
2].
Concerns stem from the use of fossil fuel combustion to provide
electricity generation and its concomitant negative externalities, which
have well known negative environmental and health impacts [
3].
For example, eliminating coal-fired air pollution alone in the United
States would prevent about 52,000 premature American deaths per year [
4]. It is estimated that information tech and services accounted for about 5% of total global electricity use and is increasing [
5].
Hence, in America, information technology electricity use is roughly
responsible for the premature deaths of 2600 Americans annually from
coal-related air pollution alone. Thus, minimizing Internet-related
energy use is of great importance.
A lot of
this Internet use is funded in some way by ads, as it has been shown
that the ad-supported Internet ecosystem is now a major part of the
United States economy [
6]. In general, online ads are viewed as undesirable by Internet users, and many people attempt to avoid them [
7]. Despite significant research on advertising avoidance [
8,
9,
10]
(which is generally focused on finding determinants of advertising
avoidance of online media to provide insights that may suggest strategic
ways to decrease advertising avoidance [
10,
11,
12,
13]), Internet users are still attempting to protect their time [
14,
15,
16].
Thus, it is not surprising that some of the most popular plugins for both Firefox [
17] and Chrome Internet browsers are for ad blocking software, which have been downloaded tens of millions of times [
18].
In addition, an increasing number of advertisers are using consumer
data and advanced digital technologies to deliver personalized
campaigns. The increasingly intrusive nature of advertising has also
raised privacy concerns [
19,
20,
21,
22]. Counter to this trend, the open source paradigm can dramatically increase trustworthiness for and autonomy of the user [
23]. Open source development methods have motivated many technologists to solve many types of technical problems [
24,
25,
26,
27,
28,
29,
30]
effectively. Of greatest importance to this study, this has resulted in
a number of open source ad blockers becoming available [
31].
Open
source ad blockers have the potential to reduce the time spent on
electricity consuming Internet-tied devices by eliminating ads. In this
study, three open source ad blockers were tested against a no-ad blocker
control for both web browsing and video watching. The time to load the
pages is recorded for browsing, and the time spent watching ads on
videos was quantified for both trending and non-trending content. From
these values, the potential for open source ad blockers to act as
effective energy conservation technologies was estimated. The results
are presented and discussed.
2. Materials and Methods
2.1. Ad Blocker Selection
Adblock Plus (
https://adblockplus.org/)
is a traditional ad blocker built as a free browser extension. However,
the developers recognize that ads finance many websites and thus offer
partial ad blocking using an “Acceptable Ads” initiative [
32]
with strict criteria that identify nonintrusive ads. Users of Adblock
Plus decide through settings the level of ads they view, but the default
is to allow “Acceptable Ads”. Another approach is taken by Privacy
Badger. Privacy Badger was developed by the Electronic Frontier
Foundation (EFF) (
https://www.eff.org/privacybadger),
which is a leading nonprofit organization focused on defending civil
liberties and privacy in the digital world. Privacy Badger is a browser
extension that automatically analyzes and blocks any tracker or ad that
violates the principle of user consent. It was designed to function
without any settings, knowledge, or configuration by the user. Finally,
uBlock Origin (It should be noted that uBlock.org, is not affiliated
with uBlock Origin. The latter which is owned by AdBlock, uses the same
“acceptable ads” method, often for which larger publishers pay a fee to
make their ads listed as acceptable.) is a wide-spectrum blocker that
blocks ads, trackers, and malware sites using (i) EasyList, (ii)
EasyPrivacy, (iii) Peter Lowe’s list of ad, tracking and malware
servers, and various lists of malware sites, including built-in filter
lists. In a previous informal study [
33] of resources and load times on a wide range of ten different ad blockers, uBlock Origin was found to be the most efficient.
2.2. Ad Blocker Testing on Webpages
Testing
the ability of an ad blocker to block ads on a specific website is
challenging because ads are served by third parties, so page load time
is dependent on essentially random external servers. To overcome this
challenge, each website listed in
was tested with no ad blocker and the three ad blockers for ten
iterations. There is still error associated with this method, so any
major outliers were removed and replaced with another page load.
includes three web search engines, two of the most used sources of
information, three news sites, and two of the top Chinese language
websites, according to Alexa [
34]. The ten page load times were recorded and averaged for each.
Table 1.
Analyzed websites and their classification.
Previous studies comparing ad blockers looked at CPU and memory use [
33],
to see if this had any discernable impact on energy use. A desktop and
laptop were monitored by a multimeter during the tests. No discernable
difference was observed on several computers, so these data were not
further analyzed. Finally, the effectiveness of the ad blockers was
determined for their ability to block ads.
2.3. Ad Blocker Testing of Streaming Video
To test the three ad blockers for their ability to screen out in-video ads on a streaming video site, YouTube (
www.youtube.com) was selected because it is the largest video website [
35].
Three trending channels were selected that would represent ad-heavy
content: (i) House of Highlights (2.29 million subscribers), (ii) Good
Mythical Morning (16.2 million subscribers), and (iii) jacksepticeye
(23.3 million subscribers). Then three non-trending channels were
selected: (i) Max and Tony (13 subscribers), (ii) Keeganchu (1.9
thousand subscribers), and (iii) Glitch (115 thousand subscribers).
Three videos were selected in each channel, and the total duration and
content duration were recorded for each. The percent ad time was
determined for no ad blocker control and the three ad blockers described
in
Section 2.1.
2.4. Energy Conservation Estimates
Annual
energy conservation potential in the United States and the world for
open source ad blockers speeding up the use of the web, Ey(USA) and Ey(Globe), can be conservatively estimated by first determining the time spent loading web pages on computers.
The time per year saved per person who uses the web in hours,
th/y, for a specific population (
pop) is given by:
𝑡ℎ/𝑦 (𝑝𝑜𝑝) [ℎ𝑜𝑢𝑟𝑠𝑦𝑒𝑎𝑟]=𝑝𝑟×𝑙ℎ×𝑤ℎ/𝑦(𝑝𝑜𝑝)×𝑡𝑙×(1ℎ𝑟3600 𝑠)×𝑠𝑏𝑙𝑜𝑐𝑘−𝑥
where
pr is the percent of time web users are rapidly clicking through the web,
lh is the page loads per hour during this rapid click time,
wh/y(pop) is the hours spent per day on the web × 365 days/year, and
tl is the average load time per page in seconds, and
sblock−x is the percent time savings using ad block
x where
x is one of the three ad blockers evaluated.
Time reports that 55% of Internet use is spent with fewer than 15 s actively on a page [
34], so the rapid clicking percent,
pr is 0.55, and the page loads per hour,
lh
is 240. In this study, two populations will be considered: (1) the
world and (2) the United States. In the United States, the time spent
using the Internet is 6 h and 31 min per day, and the world average is 6
h 42 min per day [
35]. Thus,
wh/y(U.S.) is 2372.5 h/year and
wh/y(Globe) is 2445.5 h/year. This study will provide
tl and
sblock−x for the three open source ad blockers.
The energy, Ey(pop),
used for web users to watch ads on their own devices (Please note that
any external or server-based energy conservation, which consumers do not
pay for directly, will be left for future work.) can be calculated by:
𝐸𝑦(𝑝𝑜𝑝)[𝑘𝑊ℎ𝑦𝑒𝑎𝑟]=𝑓𝑐×𝑃𝑐×𝑡ℎ/𝑦(𝑝𝑜𝑝)×𝑘𝑊1000𝑊×𝑃𝑝𝑜𝑝
where
fc is the fraction of web devices that are computers,
Pc is the power in Watts of the average computer in the population, and
Ppop
is the total population in the specific population. In 2019, the mobile
share of web use was 48%, which means the computer user fraction,
Fc, of web use is 0.52 [
35].
It was assumed here that Internet use is the same for both types of
devices. This use may differ, and the complexities of tracking that
across the globe are left for future work. To be conservative, for this
estimate for the energy used on mobile phones will be ignored and also
left for future work. Lawrence Berkeley National Lab performed a
detailed analysis of computer energy use and found the average power
draw for a desktop was 66.1 W and a laptop was 32.0 W. The overall
average was 58.5 W, which is used for
Pc [
36]. Finally, the population,
Pglobe, for the globe is made up of 4.4 billion Internet users worldwide [
35]. The United States has more than 312 million Internet users with over a 95% penetration rate [
37].
Web users that use open source ad blockers would thus save the following money,
Sy(
pop), if everyone used ad blockers:
where
re(pop) is the
electric rate for the given population. The average electric rate for
the globe is 0.14 USD/kWh and for the United States it is 0.1269 USD/kWh
[
38]. These calculations can be extended in future work by considering the costs of electricity around the world [
39].
For
non-rapid web clicking, there would also be energy saved with the ad
blockers, but it will be far less than during the rapid clicking time.
One of the times, however, when people are using the web and not
clicking frantically, is when they are watching movies, tv, music
videos, etc., via streaming video.
Finally, to
get a conservative estimate of the energy consumed for streaming video,
calculations are based on YouTube data. YouTube is the second most used
website, according to Alexa [
40]. The time YouTube watchers globally can save with ad blockers is estimated as:
where
tview is the total hours spent watching YouTube by all users globally per day, and
pads
is the percent of time streaming spent on ads. YouTube has over 2
billion users worldwide, who watch over 1 billion h of streaming videos
per day [
41], so
tview is approximately 1 billion h. The value of
pads
will be supplied by data from this study using a high and low
sensitivity bounded by the more frequently watched trending channels and
the less frequently watched non-trending channels.
The energy saving from ad blockers functioning on YouTube,
EYouTube,
can be estimated for the entire globe only because Alphabet, an ad
revenue-based company that owns Google and YouTube, does not make their
data public (Alphabet does, however, keep a significant amount of data
about their users. Users can access some of this data and possibly stop
some of the Google tracking activity following Nielo [
42]):
𝐸𝑌𝑜𝑢𝑇𝑢𝑏𝑒[𝑘𝑊ℎ𝑦𝑒𝑎𝑟]=𝑓𝑐−𝑌𝑜𝑢𝑇𝑢𝑏𝑒×𝑃𝑐×𝑡ℎ/𝑑−𝑌𝑜𝑢𝑇𝑢𝑏𝑒×365 𝑑𝑎𝑦𝑠𝑌𝑒𝑎𝑟𝑘𝑊1000𝑊
where
fc−YouTube is the fraction of web devices that are computers. Seventy percent of YouTube watch time comes from mobile devices [
41], so
fc−YouTube
is set at a conservative 0.25. For this estimate, again, the energy
used on mobile phones will be ignored and left for future work.
Finally, the economics savings potential for YouTube users that deploy an effective open source ad blocker can be estimated as:
3. Results and Discussion
All
of the open source ad blockers tested were able to eliminate at least
some ads for webpage browsing effectively. This is demonstrated with
screenshot data of two representative websites: the Weather Channel
website () and the Yahoo website () without ad blockers and the results for AdBlock+, Privacy Badger, and uBlock Origin. With no ad blocker (a),
the front page of the Weather Channel had two ads showing a horizontal
ad at the top and a box ad on the right. These ads took up enough screen
area that the trending stories are not completely visible. With
AdBlock+ enabled (b), both ads were removed, and the page enabled viewing of the trending stories. Privacy Badger, as seen in c,
also eliminated all ads from the viewer, but did not reformat, so no
additional screen area was obtained. Finally, as seen in d), uBlock Origin provided identical results to AdBlock+. This, however, was not always the case, as shown in .
Figure 1.
The impact of open source ad blockers on the Weather Channel website: (a) no ad block, (b) AdBlock+, (c) Privacy Badger, and (d) uBlock Origin.
Figure 2.
The impact of open source ad blockers on the Yahoo.com website: (a) no ad block, (b) AdBlock+, (c) Privacy Badger, and (d) uBlock Origin.
With no ad blocker (a),
the front page of Yahoo was dominated by a large horizontal ad taking
up roughly a quarter of the pages content area. With AdBlock+ enabled (b),
this large ad was removed, but a smaller horizontal ad replaced it at
the top, and an additional small ad was still visible in the middle
right-hand side of the content area. These were presumed to be
‘acceptable ads’ following the AdBlock+ business model and were
objectively less intrusive than with no ad blocker. Privacy Badger, on
the other hand, as seen in c,
eliminates all ads from the viewer as before. However, white space was
left in the place that ads were located without the ad blocker enabled.
Finally, as seen in d,
uBlock Origin not only eliminated all ads but also eliminated the white
space. This effectively provided more content per screen than all of
the other options, including those, such as Yahoo, that presumably paid
AdBlock+ to be deemed “acceptable”.
The results of the visual results indicated by the screen captures in and agree with the average page load time data, which is summarized in . For the ten websites analyzed, the average of the average page load time was 3.9 s (tl).
also shows the percent of saved load time for each open source ad
blocker. The page load time dropped 11% with AdBlock+, 22.2% with
Privacy Badger, and 28.5% with uBlock Origin, which provides sblock−x.
Clearly, uBlock Origin has the greatest potential of the three open
source ad blockers tested to save Internet users the most time.
Table 2.
Average page load times (in seconds) for no ad block, AdBlock+, Privacy
Badger, and uBlock Origin as well as the percent of saved load time for
each open source ad blocker.
However, there is other interesting information provided by that can explain the observations in and ,
AdBlock+, for example, took more time than the no ad blocker case for
Yahoo, which could be caused by the reformatting to have different ads
and/or the speed of the various servers providing those effective ads.
Whereas, for the Weather Channel, all of the open source ad blockers
tested cut down the page load time by 33 to 43 percent compared to the
no ad blocking case. Due, in large part, to the remarkable click rate of
Internet users (240 per day) during rapid browsing, the potential time
saved by enabling open source ad blockers is substantial. These values
were calculated for all of the open source ad browsers and are
summarized in .
Table 3.
Average time per year saved per person who uses the web in the world and
the United States if AdBlock+, Privacy Badger, and uBlock Origin were
used.
As can be seen in ,
the time per year saved per person who uses the web over the entire
globe using Equation (1), ranged from 38.9 h for AdBlock+ to over 100 h
using uBlock Origin. For the latter, this is equivalent to 2.5 working
weeks of productivity lost watching ads per year for the average net
citizen. In the United States. because of slightly less Internet usage
per day, the results were a few percent less. It again should be pointed
out here that this is a conservative estimate as none of the time lost
to ad loading times is included for non-rapid web surfing, which makes
up nearly half of Internet use.
The electrical
energy conserved using the three ad blockers was calculated following
Equation (2), and resultant electricity bill savings following Equation
(3) are shown in . Again, this is a conservative estimate as it not only used the conservative times from
but also only included the energy used on the fraction of computers
used and only considered the users’ side of the energy use (e.g., what
consumers would pay for in their utility bills). Despite these
conservative assumptions, both the energy conservation potential of the
various open source ad blockers as well as the economic savings for
consumers is remarkable. So, for example, the 1.35 × 10
10 kWh
saved globally for using uBlock Origin is equivalent to more than 1.0%
of the electricity generated per year from coal in the United States,
which is responsible for the premature deaths of about 52,000 American
every year from air pollution [
43,
44].
The energy savings potential in the United States from using uBlock
Origin was a much smaller 0.07%. However, if we assume that the
electricity used to power the computers to watch ads came from coal, the
reduced coal-fired pollution from using uBlock Origin could save over
36 American lives per year. Although far more pollution could be cut by
converting to renewable energy and nearly all the lives could be saved [
4,
45], 36 lives are still considerable as it is more than the number of people murdered in the 2007 Virginia Tech shooting rampage [
46]
(It has previously been questioned why the coal industry is allowed to
continue profiting from the deaths of Americans when there are ample and
lower-cost alternative sources of electricity [
47]).
Table 4.
Electrical energy conserved and consumer electric bill savings per year
from the use of open source ad blockers in the world and the United
States during rapid Internet use for AdBlock+, Privacy Badger, and
uBlock Origin.
Significant future work is needed to determine the
energy conservation potential on the server side. Data servers use a
substantial and growing amount of energy, and there is considerable
effort to reduce that energy use [
48,
49,
50] using both thermal management [
51,
52,
53,
54], electrical management [
55], configuration [
56], and smart systems [
57]. The concept of green data centers [
58,
59] is growing. Facebook open sourced their designs [
60], and there is even open hardware that could be used to help monitor and improve them [
61].
Despite this effort, the results of this study indicate some of the
lowest-hanging fruit is simply to eliminate the need for some servers by
expanding the deployment of open source ad blockers. Future work should
consider policies to encourage this deployment for energy conservation
alone, although there are clearly arguments for saving Internet users’
time as well as consumers’ money.
Economically
the savings for the use of open source ad blockers are potentially
easier to understand. For example, in the United States, if all Internet
users enabled Privacy Badger on their computers, they would expect to
save more than 91 million dollars annually. Globally, if all Internet
users used uBlock Origin, they would collectively save more than 1.8
billion U.S. dollars a year.
These savings ()
only considered the rapid page loading portion of Internet users’ time
spent on the web. Much of the remainder of the time is spent streaming
videos in general and YouTube in particular. Due to the lack of data on
what ratio of YouTube ad time is spent on trending and non-trending
content, a sensitivity between the two resulted in a minimum estimated
ad time of 0.06% up to 21%. Time spent watching YouTube ads determined
using Equation (4) ranges from 600,000 h/day 210 billion h/day globally.
EYouTube from Equation (5)
ranges from 3.6 million to 1.13 billion kWh/year, and the economic
savings from using uBlock Origin would range from just under half a
million dollars to $158 million per year globally. This
obviously is an enormous range due to the uncertainty of YouTube viewer
habits and the ad algorithms. The YouTube analysis can be treated as a
preliminary study just to determine if future work is warranted. It
appears to be the case as that if the majority of YouTube viewing is for
trending or popular videos that have substantial ads, both the energy
used and money spent watching them on electricity could be on the order
of 8% of the expenditures for rapid web browsing from .
Although
this study had clear limitations on both the size and scope (e.g.,
number of open source ad blockers analyzed as well as websites
analyzed), elasticity of Internet use (e.g., consumers may not reduce
their Internet use by the time saved from not having to allow ads to
load), and access to information (e.g., YouTube ad algorithms and users’
statistics), the preliminary results were enough to provide interesting
insights into the use of open source software for energy conservation.
Historically the use of free and open source software aimed at energy
conservation could be grouped into broad categories of software for
green computing and manufacturing [
62,
63], simulation [
64], energy conservation education and knowledge dissemination [
65,
66,
67], or energy conservation controls [
68,
69].
Now those that fund energy conservation efforts, such as the Department
of Energy in the United States, may want to look closely at the return
on investment for investing directly in open source development of
software and hardware that consumers have an incentive to use to
maximize returns on public funding [
70,
71,
72,
73,
74].
Providing increased economic motivation for free and open source
software development to maintain the virtual arms race with well-funded
advertising-based companies may be needed [
75,
76,
77]. As Malloy et al. pointed out, “ad blockers are a formidable threat” to online advertising [
78], and efforts to thwart their effectiveness are underway [
77,
79]. Overall, the evaluations in this study agreed with the effectiveness of ad blockers seen before [
80], and as ads annoy users [
81], ad blocker use can be expected to continue to expand [
82].
To accelerate the pace of this expansion, further work is needed in
both informing Internet users about the costs of advertising (both in
Internet users’ time as well as energy and environmental impact) as well
as the technical development of ad blockers to win the “virtual arms
race” with advertisers. For countries looking for easy and low-cost
energy conservation measures, opportunities to implement policies to
encourage the development and deployment of open source ad blockers are
clear from the results of this study.
4. Conclusions
This
study, although preliminary, clearly showed enormous potential for open
source ad blockers to reduce consumer time waiting for Internet ads to
load as well as the electricity needed to run their computers (and other
electronic technologies) during that time. In addition, the
externalities (including premature fatalities) associated with
fossil-fuel-based electricity spent using computers by eliminating ads
during Internet browsing and video streaming would be reduced. The
results show that page load time was reduced for all open source ad
blockers: 11% with AdBlock+, 22.2% with Privacy Badger, and 28.5% with
uBlock Origin. Strikingly, uBlock Origin has the potential to save the
average global Internet user more than 100 h annually. The energy
conserved if everyone in the United States used an open source ad
blocker would save over 36 Americans lives per year if it were to offset
coal-fired electricity generated-based pollution. Similarly, in the
United States, if all American Internet users enabled Privacy Badger on
their computers, Americans would save more than $91 million
annually. Globally, the results with the most efficient open source ad
blocker tested, uBlock Origin, would be even more substantial: ad
blocking would save consumers more than $1.8 billion/year. It is clear from this study that open source ad blockers are an effective technology for energy conservation.
Funding
This research was funded by the Witte Endowment.
Acknowledgments
The author would like to acknowledge technical assistance and helpful discussions with E. Garvey.
Conflicts of Interest
The author declares no conflict of interest.
References
- Cole, J.I.; Suman, M.; Schramm, P.; Zhou, L. Surveying the Digital Future. 2017. Available online: http://www.digitalcenter.org/wp-content/uploads/2013/10/2017-Digital-Future-Report.pdf (accessed on 29 March 2020).
- Aslan, J.; Mayers, K.; Koomey, J.G.; France, C. Electricity Intensity of Internet Data Transmission: Untangling the Estimates. J. Ind. Ecol. 2018, 22, 785–798. [Google Scholar] [CrossRef]
- Epstein,
P.; Buonocore, J.; Eckerle, K.; Hendryx, M.; Stout, B.; Heinberg, R.;
Clapp, R.; May, B.; Reinhart, N.; Ahern, M.; et al. Full cost accounting
for the life cycle of coal. Ann. N. Y. Acad. Sci. 2011, 1219, 73–98. [Google Scholar] [CrossRef] [PubMed]
- Prehoda, E.W.; Pearce, J.M. Potential lives saved by replacing coal with solar photovoltaic electricity production in the U.S. Renew. Sustain. Energy Rev. 2017, 80, 710–715. [Google Scholar] [CrossRef][Green Version]
- Van
Heddeghem, W.; Lambert, S.; Lannoo, B.; Colle, D.; Pickavet, M.;
Demeester, P. Trends in worldwide ICT electricity consumption from 2007
to 2012. Comput. Commun. 2014, 50, 64–76. [Google Scholar] [CrossRef][Green Version]
- Deighton, J.; Kornfeld, L.; Gerra, M. Economic Value of the Advertising-Supported Internet Ecosystem. 2017. Available online: https://www.iab.com/wp-content/uploads/2017/03/Economic-Value-Study-2017-FINAL2.pdf (accessed on 29 March 2020).
- Li, W.; Huang, Z. The Research of Influence Factors of Online Behavioral Advertising Avoidance. Am. J. Ind. Bus. Manag. 2016, 6, 947. [Google Scholar] [CrossRef]
- Smith, R.E.; Swinyard, W.R. Information Response Models: An Integrated Approach. J. Mark. 1982, 46, 81–93. [Google Scholar] [CrossRef]
- Malhotra, N.K.; King, T. Don’t negate the whole field. Mark. Res. 2003, 15, 43. [Google Scholar]
- Pasadeos, Y. Perceived Informativeness of and Irritation with Local Advertising. J. Mass Commun. Q. 2016, 67, 35–39. [Google Scholar] [CrossRef]
- Rodgers,
S.; Wang, Y.; Ruth, R.; Frank, A. The Web Motivation Inventory:
Replication, Extension, and Application to Internet Advertising. Int. J. Advert. 2007, 26, 447–476. [Google Scholar] [CrossRef]
- White,
T.B.; Zahay, D.L.; Thorbjørnsen, H.; Shavitt, S. Getting too personal:
Reactance to highly personalized email solicitations. Mark. Lett. 2008, 19, 39–50. [Google Scholar] [CrossRef]
- Roberts, M.L.; Zahay, D. Internet Marketing: Integrating Online and Offline Strategies; Cengage Learning: Boston, MA, USA, 2012; ISBN 978-1-285-40203-1. [Google Scholar]
- Milne, G.R.; Boza, M.-E. Trust and Concern in Consumers’ Perceptions of Marketing Information Management Practices. J. Interact. Mark. 1999, 13, 5–24. [Google Scholar] [CrossRef]
- Nowak, G.J.; Joseph, P. Understanding Privacy Concerns: An Assessment of Consumers’ Information-Related Knowledge and Belief. J. Direct Mark. 1992, 6, 28–39. [Google Scholar] [CrossRef]
- Gurau,
C.; Ashok, R.; Claire, G. To Legislate or Not to Legislate: A
Comparative Study of Privacy/Personalization Factors Affecting French,
UK and US Web Sites. J. Consum. Mark. 2003, 20, 652–664. [Google Scholar] [CrossRef]
- Miller, F. The 14 Best Mozilla Firefox Add-ons, Apps & Extensions of 2020. Product. Land 2019. Available online: https://productivityland.com/best-firefox-add-ons-extensions/ (accessed on 23 February 2020).
- Spadafora, A. These are the most popular Google Chrome extensions. Tech. Radar 2019. Available online: https://www.techradar.com/news/most-popular-google-chrome-extensions (accessed on 22 February 2020).
- Evans, D.S. The Online Advertising Industry: Economics, Evolution, and Privacy. J. Econ. Perspect. 2009, 23, 37–60. [Google Scholar] [CrossRef][Green Version]
- Vega, T. For Online Privacy, Click Here. The New York Time. 19 January 2012. Available online: https://www.nytimes.com/2012/01/20/business/media/the-push-for-online-privacy-advertising.html (accessed on 29 February 2020).
- Lipman, R. Online Privacy and the Invisible Market for Our Data. Penn St. L. Rev. 2015, 120, 777. [Google Scholar]
- Hawthorne-Castro, J. Digital Advertising and Consumer Privacy: Three Trends to Watch. Mar. Tech. Advis. 2018. Available online: https://www.martechadvisor.com/articles/ads/digital-advertising-and-consumer-privacy-three-trends-to-watch/ (accessed on 22 February 2020).
- Hansen,
M.; Köhntopp, K.; Pfitzmann, A. The Open Source approach—opportunities
and limitations with respect to security and privacy. Comput. Secur. 2002, 21, 461–471. [Google Scholar] [CrossRef]
- Feller, J.; Fitzgerald, B. Understanding Open Source Software Development; Addison-Wesley: London, UK, 2002; pp. 143–159. [Google Scholar]
- Lakhani,
K.R.; Wolf, R.G. Why hackers do what they do: Understanding motivation
and effort in free/open source software projects. Perspect. Free Open Source Softw. 2005, 1, 3–22. [Google Scholar] [CrossRef]
- Hippel, E.V.; Krogh, G.V. Open source software and the “private-collective” innovation model: Issues for organization science. Organ. Sci. 2003, 14, 209–223. [Google Scholar] [CrossRef][Green Version]
- Pearce, J.M. Building Research Equipment with Free, Open-Source Hardware. Science 2012, 337, 1303–1304. [Google Scholar] [CrossRef] [PubMed]
- Pearce, J. Open-Source Lab: How to Build Your Own Hardware and Reduce Research Costs, 1st ed.; Elsevier: Waltham, MA, USA, 2014. [Google Scholar]
- Gibb, A.; Abadie, S. Building Open Source Hardware: DIY Manufacturing for Hackers and Makers, 1st ed.; Addison-Wesley Professional: Boston, MA, USA, 2014. [Google Scholar]
- Oberloier, S.; Pearce, J.M. General Design Procedure for Free and Open-Source Hardware for Scientific Equipment. Designs 2018, 2, 2. [Google Scholar] [CrossRef][Green Version]
- Open Source AdBlock Alternatives-AlternativeTo.net. Available online: https://alternativeto.net/software/adblock-for-chrome/?license=opensource (accessed on 22 February 2020).
- Allowing Acceptable Ads in Adblock Plus. Available online: https://adblockplus.org/en/acceptable-ads (accessed on 27 February 2020).
- 10 Ad Blocking Extensions Tested for Best Performance • Raymond.CC. Available online: https://www.raymond.cc/blog/10-ad-blocking-extensions-tested-for-best-performance/ (accessed on 27 February 2020).
- Haile, T. What You Think You Know About the Web Is Wrong. Time. 2014. Available online: https://time.com/12933/what-you-think-you-know-about-the-web-is-wrong/ (accessed on 29 February 2020).
- Kemp, S. Digital trends 2019: Every Single Stat You Need to Know about the Internet. Available online: https://thenextweb.com/contributors/2019/01/30/digital-trends-2019-every-single-stat-you-need-to-know-about-the-internet/ (accessed on 29 February 2020).
- Desroches,
L.-B.; Fuchs, H.; Greenblatt, J.; Pratt, S.; Willem, H.; Claybaugh, E.;
Beraki, B.; Nagaraju, M.; Price, S.; Young, S. Computer Usage and National Energy Consumption: Results from a Field-Metering Study; Lawrence Berkeley National Lab. (LBNL): Berkeley, CA, USA, 2014.
- United States Internet Usage and Population State by State. Available online: https://www.internetworldstats.com/unitedstates.htm (accessed on 3 March 2020).
- EIA-Electricity Data. Available online: https://www.eia.gov/electricity/monthly/epm_table_grapher.php?t=epmt_5_6_a (accessed on 3 March 2020).
- Electricity Prices around the World | GlobalPetrolPrices.com. Available online: https://www.globalpetrolprices.com/electricity_prices/ (accessed on 3 March 2020).
- Alexa-Top Sites. Available online: https://www.alexa.com/topsites (accessed on 3 March 2020).
- YouTube. Press–YouTube. Available online: https://www.youtube.com/about/press/ (accessed on 3 March 2020).
- Nielo, D. All the Ways Google Tracks You—And How to Stop It. Wired. 27 May 2019. Available online: https://www.wired.com/story/google-tracks-you-privacy/ (accessed on 3 March 2020).
- Caiazzo,
F.; Ashok, A.; Waitz, I.A.; Yim, S.H.L.; Barrett, S.R.H. Air pollution
and early deaths in the United States. Part I: Quantifying the impact of
major sectors in 2005. Atmos. Environ. 2013, 79, 198–208. [Google Scholar] [CrossRef]
- Dedoussi,
I.C.; Barrett, S.R.H. Air pollution and early deaths in the United
States. Part II: Attribution of PM2.5 exposure to emissions species,
time, location and sector. Atmos. Environ. 2014, 99, 610–617. [Google Scholar] [CrossRef]
- Burney, J.A. The downstream air pollution impacts of the transition from coal to natural gas in the United States. Nat. Sustain. 2020, 3, 152–160. [Google Scholar] [CrossRef]
- Perry, M. Massacre sparks foreign criticism of U.S. gun culture. Reuters. 17 April 2007. Available online: https://www.reuters.com/article/us-usa-crime-shooting-world/massacre-sparks-foreign-criticism-of-u-s-gun-culture-idUSL1752333820070417 (accessed on 3 March 2020).
- Pearce, J.M. Towards Quantifiable Metrics Warranting Industry-Wide Corporate Death Penalties. Soc. Sci. 2019, 8, 62. [Google Scholar] [CrossRef][Green Version]
- Gizli, V.; Marx Gómez, J. A Framework to Optimize Energy Efficiency in Data Centers Based on Certified KPIs. Technologies 2018, 6, 87. [Google Scholar] [CrossRef][Green Version]
- Ni, J.; Bai, X. A review of air conditioning energy performance in data centers. Renew. Sustain. Energy Rev. 2017, 67, 625–640. [Google Scholar] [CrossRef]
- Choo, K.; Galante, R.M.; Ohadi, M.M. Energy consumption analysis of a medium-size primary data center in an academic campus. Energy Build. 2014, 76, 414–421. [Google Scholar] [CrossRef]
- Singh,
R.; Mochizuki, M.; Mashiko, K.; Nguyen, T. Heat pipe based cold energy
storage systems for datacenter energy conservation. Energy 2011, 36, 2802–2811. [Google Scholar] [CrossRef]
- Bai,
Y.; Gu, L.; Qi, X. Comparative Study of Energy Performance between Chip
and Inlet Temperature-Aware Workload Allocation in Air-Cooled Data
Center. Energies 2018, 11, 669. [Google Scholar]
- Priyadumkol, J.; Kittichaikarn, C. Application of the combined air-conditioning systems for energy conservation in data center. Energy Build. 2014, 68, 580–586. [Google Scholar] [CrossRef]
- Meng,
X.; Zhou, J.; Zhang, X.; Luo, Z.; Gong, H.; Gan, T. Optimization of the
thermal environment of a small-scale data center in China. Energy 2020, 196, 117080. [Google Scholar] [CrossRef]
- Mitchell,
R.B.; York, R. Reducing the web’s carbon footprint: Does improved
electrical efficiency reduce webserver electricity use? Energy Res. Soc. Sci. 2020, 65, 101474. [Google Scholar] [CrossRef]
- Liu,
Z.; Yu, H.; Liu, R.; Wang, M.; Li, C. Configuration Optimization Model
for Data-Center-Park-Integrated Energy Systems under Economic,
Reliability, and Environmental Considerations. Energies 2020, 13, 448. [Google Scholar] [CrossRef][Green Version]
- Fernández-Cerero, D.; Fernández-Montes, A.; Velasco, F. Productive Efficiency of Energy-Aware Data Centers. Energies 2018, 11, 2053. [Google Scholar] [CrossRef][Green Version]
- Bahari,
H.I.; Shariff, S.S.M. Review on data center issues and challenges:
Towards the Green Data Center. In Proceedings of the 2016 6th IEEE
International Conference on Control System, Computing and Engineering
(ICCSCE), Penang, Malaysia, 25–27 November 2016; pp. 129–134. [Google Scholar]
- Zafar,
S.; Chaudhry, S.A.; Kiran, S. Adaptive TrimTree: Green Data Center
Networks through Resource Consolidation, Selective Connectedness and
Energy Proportional Computing. Energies 2016, 9, 797. [Google Scholar] [CrossRef][Green Version]
- Building Efficient Data Centers with the Open Compute Project | Facebook. Available online: https://www.facebook.com/notes/facebook-engineering/building-efficient-data-centers-with-the-open-compute-project/10150144039563920/ (accessed on 4 March 2020).
- Oberloier, S.; Pearce, J.M. Open source low-cost power monitoring system. HardwareX 2018, 4, e00044. [Google Scholar] [CrossRef]
- Fakhar, F.; Javed, B.; ur Rasool, R.; Malik, O.; Zulfiqar, K. Software level green computing for large scale systems. J. Cloud Comput. 2012, 1, 4. [Google Scholar] [CrossRef][Green Version]
- Byard,
D.J.; Woern, A.L.; Oakley, R.B.; Fiedler, M.J.; Snabes, S.L.; Pearce,
J.M. Green fab lab applications of large-area waste polymer-based
additive manufacturing. Addit. Manuf. 2019, 27, 515–525. [Google Scholar] [CrossRef][Green Version]
- Stifter,
M.; Widl, E.; Andrén, F.; Elsheikh, A.; Strasser, T.; Palensky, P.
Co-simulation of components, controls and power systems based on open
source software. In Proceedings of the 2013 IEEE Power Energy Society
General Meeting, Vancouver, BC, Canada, 21–25 July 2013; pp. 1–5. [Google Scholar]
- Brewer,
R.S.; Lee, G.E.; Johnson, P.M. The Kukui Cup: A Dorm Energy Competition
Focused on Sustainable Behavior Change and Energy Literacy. In
Proceedings of the 2011 44th Hawaii International Conference on System
Sciences, Koloa, HI, USA, 4–7 January 2011; pp. 1–10. [Google Scholar]
- Leslie,
P.; Pearce, J.M.; Harrap, R.; Daniel, S. The application of smartphone
technology to economic and environmental analysis of building energy
conservation strategies. Int. J. Sustain. Energy 2012, 31, 295–311. [Google Scholar] [CrossRef][Green Version]
- Zelenika,
I.; Pearce, J.M. The Internet and other ICTs as tools and catalysts for
sustainable development: Innovation for 21st century. Inf. Dev. 2013, 29, 217–232. [Google Scholar] [CrossRef]
- Wen,
Y.-J.; Bonnell, J.; Agogino, A.M. Energy Conservation Utilizing
Wireless Dimmable Lighting Control in a Shared-Space Office. In
Proceedings of the Illuminating Engineering Society of North America
Annual Conference 2008, Savannah, GA, USA, 10–11 November 2008; pp.
97–108. [Google Scholar]
- Capehart, B.L.; Middelkoop, T. Handbook of Web Based Energy Information and Control Systems; The Fairmont Press, Inc.: Lilburn, GA, USA, 2011; ISBN 978-0-88173-669-4. [Google Scholar]
- O’Reilly, T. Lessons from open-source software development. Commun. ACM 1999, 42, 32–37. [Google Scholar] [CrossRef]
- Guhlin, M. Open Source and ROI: Open Source Has Made Significant Leaps in Recent Years. What Does It Have to Offer Education? Technol. Learn. 2007, 27, 12. [Google Scholar]
- Pearce, J.M. Return on investment for open source scientific hardware development. Sci. Public Policy 2016, 43, 192–195. [Google Scholar] [CrossRef]
- Coakley, M.; Hurt, D.E. 3D Printing in the Laboratory: Maximize Time and Funds with Customized and Open-Source Labware. J. Lab. Autom. 2016, 21, 489–495. [Google Scholar] [CrossRef][Green Version]
- Pearce, J.M. Maximizing Returns for Public Funding of Medical Research with Opensource Hardware. Health Policy Technol. 2017, 6, 381. [Google Scholar] [CrossRef][Green Version]
- Riehle, D. The Economic Motivation of Open Source Software: Stakeholder Perspectives. Computer 2007, 40, 25–32. [Google Scholar] [CrossRef]
- Linåker,
J.; Munir, H.; Wnuk, K.; Mols, C.E. Motivating the contributions: An
Open Innovation perspective on what to share as Open Source Software. J. Syst. Softw. 2018, 135, 17–36. [Google Scholar] [CrossRef]
- Mughees, M.H.; Qian, Z.; Shafiq, Z.; Dash, K.; Hui, P. A First Look at Ad-block Detection: A New Arms Race on the Web. arXiv 2016, arXiv:1605.05841. [Google Scholar]
- Malloy,
M.; McNamara, M.; Cahn, A.; Barford, P. Ad Blockers: Global Prevalence
and Impact. In Proceedings of the 2016 ACM on Internet Measurement
Conference-IMC ’16, Santa Monica, CA, USA, 14–16 November 2016; ACM
Press: Santa Monica, CA, USA, 2016; pp. 119–125. [Google Scholar]
- Bashir,
M.A.; Arshad, S.; Kirda, E.; Robertson, W.; Wilson, C. How Tracking
Companies Circumvented Ad Blockers Using WebSockets. In Proceedings of
the Internet Measurement Conference 2018- IMC ’18, Boston, MA, USA, 31
October–2 November 2018; ACM Press: Boston, MA, USA, 2018; pp. 471–477. [Google Scholar]
- Post,
E.L.; Sekharan, C.N. Comparative Study and Evaluation of Online
Ad-Blockers. In Proceedings of the 2015 2nd International Conference on
Information Science and Security (ICISS), Seoul, Korea, 14–16 December
2015; IEEE: Seoul, Korea, 2015; pp. 1–4. [Google Scholar]
- Pujol,
E.; Hohlfeld, O.; Feldmann, A. Annoyed Users: Ads and Ad-Block Usage in
the Wild. In Proceedings of the 2015 ACM Conference on Internet
Measurement Conference-IMC ’15, Tokyo, Japan, 28–30 October 2015; ACM
Press: Tokyo, Japan, 2015; pp. 93–106. [Google Scholar]
- Redondo,
I.; Aznar, G. To use or not to use ad blockers? The roles of knowledge
of ad blockers and attitude toward online advertising. Telemat. Inform. 2018, 35, 1607–1616. [Google Scholar] [CrossRef][Green Version]