Characteristics of the Digital Content about Energy-Saving in Different Countries around the World
Abstract
:1. Introduction
2. Materials and Methods
2.1. Research Questions
- What are the topics more related to the query “energy-saving” that people search for on Google in different countries? The assumption informing this question was that the topics related to the query for “energy-saving” in different countries can, in some proportion, reflect the topics with which Google users relate the issue of energy-saving in each of the different countries. Information provided by Google Trends was used to answer the question, and the results of the query were grouped by region;
- What are the kinds of webpages that people found when they look for “energy-saving” on Google in the different countries? The assumption of this question is that the ranking that Google displays of different kinds of sites can, to some extent, reflect the level of access to information on these sites in the respective countries. This variable is important in that it can provide clues about the type of information that each country consumes when conducting the query “energy-saving”;
- What are the kinds of images that people found when they looked for “energy-saving” on Google in the different countries? The premise of this question is that the best-ranked images from Google Search can be an indicator of the type of information that is most often related to the topic of energy-saving in different countries.
2.2. Participants
- By region: Asia (3); Europe (3); Latin America (6); North America (2); Oceania (2); Africa (1)
- By language: English (10); Spanish (7)
- By region: North America (5.5/9.08%); Europe (7/8.78%); Oceania (7/8.67%); Latin America (7.67/8.80%); Asia (8.33/8.83%); Africa (9/8.84%)
- By language: English (7.3/8.85%); Spanish (7.57/8.78%)
2.3. Procedure
- Topics on Google Trends related to the query “energy-saving.” This refers to terms associated with the query “energy-saving” or “Ahorro de energía” (for the queries in Spanish) according to Google Trends. To obtain these values, we accessed https://trends.google.com/trends/, customized the query for each country of the sample, and limited the span to the year 2017 for the web search. Once we had obtained the results, we integrated them into the three levels of analysis (per country, general, and region);
- Types of Webpages found when searching “energy-saving” on Google. This category comprises the types of websites that were found using Google Advanced Search. To do that, we accessed the site https://www.google.com/advanced_search, selected the language and region, and then ran the query “Energy-saving” or “Ahorro de energía” (for the queries in Spanish). The first 10 websites were selected from the results in order of importance, and the type of site was manually analyzed and classified under the following categories: (a) Educational; (b) commercial; (c) non-profit organizations; (d) media; (e) government; and (f) Wikipedia. We then integrated the results into the three levels of analysis (per country, general, and region);
- Characteristics of images found by Google Search with the keyword “energy-saving.” This category refers to the features shared by the top 400 images that users obtain when they search “energy-saving” for a specific country. The query “energy-saving” was searched in Google Images (advanced search) with the specific language and country for each member of the sample. Afterward, the top 400 images returned by Google were downloaded using the Chrome extension GetThemAll. Images unrelated to the search (i.e., the Google logo) were discarded. Then, the images were labeled with Cloud Vision, using the Application Programming Interface (API) memespector-python [26]. Figure 1 shows a real example of how Google Cloud Vision identifies the characteristics of a specific image. Later, to reduce the possible wrong labels that could be given as the output [27], the complete list of labels was reviewed manually and those found to be irrelevant were removed. Thereupon, the most representative labels (95 percentile) were identified, and the constant comparison method was employed to identify the main categories found on labels. Finally, manual categorization of a sample of the first 50 images of each country (n = 1450) was conducted.
3. Results
3.1. What Are the Topics More Related to the Query “Energy-Saving” That People Search on Google in the Different Countries?
3.2. What Are the Kinds of Webpages That People Found When They Looked for “Energy-Saving” on Google in the Different Countries?
- Educational: sites that aim at increasing the level of understanding of their users about energy-saving and its impact on different spheres (e.g., economic, environmental, etc.);
- Commercial: sites that aim to sell a product or provide a service that relates to the topic of energy-saving;
- Non-profit organizations: sites that are independent of government insight, including non-governmental organizations;
- Media: any official newspaper or related media sites with news regarding energy-saving (e.g., online newspapers, magazines);
- Government: sites with a governmental URL domain in accordance with each country;
- Wikipedia: any URL from this online encyclopedia.
3.3. What Are the Kinds of Images That People Found When They Looked for “Energy-Saving” on Google in the Different Countries?
- Electronic devices: images that have the use of electricity and/or various electronic elements for their functioning in common. Examples of this category include speakers, mobile phones, tablets, computers, laptops, washing machines, televisions, screens, switches, monitors, mini splits, batteries, and power sources. This category also includes energy generation devices such as solar cells, wind turbines, and windmills;
- Human: shapes or images that allude to the human figure, such as human shapes and human body parts like hands, fingers, eyes, and fingerprints, among others;
- Nature: shapes or figures that allude to elements of nature like the sun, trees, leaves, plants, grass, rain, wind, and diverse flora and fauna, among others;
- Infographics: figures or images that represent a collection of information in a graphic manner. Examples of this include infographics with statistics, processes, geography, characteristics, comparisons, and varieties, among others;
- Products: objects or images in which the main component is a product or service that is displayed for commercial purposes; this image makes a clear and direct reference to a brand, a package, or a set of characteristics thereof (e.g., solar cells, energy-saving lamps, and lamp batteries);
- Money: refers to images where the focus involves economic exchange and trade agents, such as coins, bills, peso signs, payments, invoices, and piggy banks, as well as other symbols related to savings and the use of money;
- Illumination: comprises images or objects that refer to shapes of/or equipment that provides artificial lightning. Examples include spotlights or electric bulbs, street lighting, lamps, and any other objects that produce artificial light;
- Home: images or figures that allude to the various elements that make up a home (e.g., the shape of a house, kitchens, living or dining rooms, bedrooms, garages, bathrooms, furniture, or decorations);
- Transportation: includes figures or elements that refer to various vehicles or means of transporting people or objects (e.g., bicycles, cars, buses, trucks, trams, trains, etc.).
4. Discussion and Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- OECD. Compendium of OECD Well-Being Indicators. Available online: https://www.oecd.org/general/compendiumofoecdwell-beingindicators.htm (accessed on 5 August 2019).
- He, W.; Fang, Y.; Malekian, R.; Li, Z. Time Series Analysis of Online Public Opinions in Colleges and Universities and its Sustainability. Sustainability 2019, 11, 3546. [Google Scholar] [CrossRef]
- Van Dijck, J. In data we trust? The implications of datafication for social monitoring. MATRIZes 2017, 11, 39–59. [Google Scholar] [CrossRef] [Green Version]
- Brunn, S. Cyberspace Knowledge Gaps and Boundaries in Sustainability Science: Topics, Regions, Editorial Teams and Journals. Sustainability 2014, 6, 6576–6603. [Google Scholar] [CrossRef] [Green Version]
- Wilson, E.O. Biophilia; Harvard University Press: Cambridge, MA, USA, 1984; p. 176. ISBN 978-0-674-07442-2. [Google Scholar]
- Beatley, T. Biophilic Cities: Integrating Nature into Urban Design and Planning, 2nd ed.; Island Press: Washington, DC, USA, 2010; ISBN 978-1-59726-715-1. [Google Scholar]
- Prati, G.; Albanesi, C.; Pietrantoni, L. Social Well-Being and Pro-Environmental Behavior: A Cross-Lagged Panel Design. Hum. Ecol. Rev. 2017, 23, 123–140. [Google Scholar] [CrossRef]
- Hori, S.; Kondo, K.; Nogata, D.; Ben, H. The determinants of household energy-saving behavior: Survey and comparison in five major Asian cities. Energy Policy 2013, 52, 354–362. [Google Scholar] [CrossRef]
- Kollmuss, A.; Agyeman, J. Mind the Gap: Why do people act environmentally and what are the barriers to pro-environmental behavior? Environ. Educ. Res. 2002, 8, 239–260. [Google Scholar] [CrossRef] [Green Version]
- World Commission on Environment and Development. Our Common Future; Oxford University Press: Oxford, UK; New York, NY, USA, 1987; ISBN 978-0-19-282080-8. [Google Scholar]
- Strange, T.; Bayley, A. OECD Insights: Sustainable Development: Linking Economy, Society, Environment; OECD: Paris, France, 2009; ISBN 978-92-64-04778-5. [Google Scholar]
- Mittal, M.K.; Kirar, N.; Meena, J. Implementation of Search Engine Optimization: Through White Hat Techniques. In Proceedings of the 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), Greater Noida (UP), India, 12–13 October 2018; pp. 674–678. [Google Scholar]
- Search Engine Optimization. Available online: https://www.optimizely.com/optimization-glossary/search-engine-optimization/ (accessed on 5 August 2019).
- Search Engine Market Share Worldwide. Available online: http://gs.statcounter.com/search-engine-market-share (accessed on 5 August 2019).
- Zaghoul, F.A.; Rababah, O.; Fakhouri, H. Website Search Engine Optimization: Geographical and Cultural Point of View. In Proceedings of the 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, Cambridge, UK, 26–28 March 2014; pp. 452–455. [Google Scholar]
- Rogers, R. Digital Methods for Web Research. In Emerging Trends in the Social and Behavioral Sciences; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2015; pp. 1–22. ISBN 978-1-118-90077-2. [Google Scholar]
- Rogers, R. Digital Methods; The MIT Press: Cambridge, MA, USA, 2015; ISBN 978-0-262-52824-5. [Google Scholar]
- Gillian, R. Visual Methodologies: An Introduction to Researching with Visual Materials; SAGE Publications: Thousand Oaks, CA, USA, 2016; ISBN 978-1-4739-6791-5. [Google Scholar]
- Perdue, R.T.; Hawdon, J.; Thames, K.M. Can Big Data Predict the Rise of Novel Drug Abuse? J. Drug Issues 2018, 48, 508–518. [Google Scholar] [CrossRef]
- Park, S.; Kim, J. The effect of interest in renewable energy on US household electricity consumption: An analysis using Google Trends data. Renew. Energy 2018, 127, 1004–1010. [Google Scholar] [CrossRef]
- NetMarketShare. Market Share for Mobile, Browsers, Operating Systems and Search Engines. Available online: https://netmarketshare.com/ (accessed on 5 August 2019).
- Vaughan, L. Discovering business information from search engine query data. Online Inf. Rev. 2014, 38, 562–574. [Google Scholar] [CrossRef]
- Green, H.K.; Edeghere, O.; Elliot, A.J.; Cox, I.J.; Morbey, R.; Pebody, R.; Bone, A.; McKendry, R.A.; Smith, G.E. Google search patterns monitoring the daily health impact of heatwaves in England: How do the findings compare to established syndromic surveillance systems from 2013 to 2017? Environ. Res. 2018, 166, 707–712. [Google Scholar] [CrossRef] [PubMed]
- Mulfari, D.; Celesti, A.; Fazio, M.; Villari, M.; Puliafito, A. Using Google Cloud Vision in assistive technology scenarios. In Proceedings of the 2016 IEEE Symposium on Computers and Communication (ISCC), Messina, Italy, 27–30 June 2016; pp. 214–219. [Google Scholar]
- Hyam, R. Automated Image Sampling and Classification Can Be Used to Explore Perceived Naturalness of Urban Spaces. PLoS ONE 2017, 12, e0169357. [Google Scholar] [CrossRef] [PubMed]
- Mintz, A. Memespector (Python Version). Available online: https://github.com/amintz/memespector-python (accessed on 5 August 2019).
- Hosseini, H.; Xiao, B.; Poovendran, R. Google’s Cloud Vision API is Not Robust to Noise. In Proceedings of the 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), Cancun, Mexico, 18–21 December 2017; pp. 101–105. [Google Scholar]
# | Country | Language | Region | Environment Importance | Position of Importance |
---|---|---|---|---|---|
1 | Argentina | Spanish | Latin America | 8.76% | 8 |
2 | Australia | English | Oceania | 7.92% | 9 |
3 | Canada | English | North America | 9.03% | 6 |
4 | Chile | Spanish | Latin America | 9.14% | 6 |
5 | Colombia | Spanish | Latin America | 9.07% | 6 |
6 | Hong Kong | English | Asia | 8.75% | 9 |
7 | India | English | Asia | 8.83% | 8 |
8 | Ireland | English | Europe | 8.62% | 8 |
9 | Mexico | Spanish | Latin America | 8.78% | 9 |
10 | New Zealand | English | Oceania | 9.42% | 5 |
11 | Peru | Spanish | Latin America | 8.74% | 8 |
12 | Singapore | English | Asia | 8.91% | 8 |
13 | South Africa | English | Africa | 8.84% | 9 |
14 | Spain | Spanish | Europe | 8.67% | 7 |
15 | United Kingdom | English | Europe | 9.06% | 6 |
16 | United States | English | North America | 9.13% | 5 |
17 | Venezuela | Spanish | Latin America | 8.28% | 9 |
Topic | Canada | USA | North America | Spain | UK | Ireland | Europe | Australia | New Zeland | Oceania | Hong Kong | India | Singapur | Asia | Argentina | Chile | Colombia | Mexico | Peru | Latinamerica | South Africa | Africa | Average |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Energy conservation | 88 | 89 | 89 | 81 | 100 | 91 | 75 | 86 | 81 | 100 | 84 | 55 | 80 | 94 | 94 | 85 | |||||||
Energy efficiency | 53 | 53 | 93 | 33 | 74 | 70 | 50 | 64 | 61 | ||||||||||||||
Saving | 21 | 17 | 19 | 67 | 37 | 6 | 37 | 13 | 7 | 10 | 11 | 20 | 9 | 13 | 100 | 67 | 96 | 74 | 75 | 82 | 12 | 12 | 38 |
Electric energy | 18 | 12 | 15 | 15 | |||||||||||||||||||
Lighting | 11 | 16 | 14 | 26 | 19 | 23 | 12 | 21 | 17 | 5 | 9 | 7 | 15 | 15 | 15 | ||||||||
Money | 4 | 4 | 25 | 25 | 15 | ||||||||||||||||||
Incandescent light bulb | 5 | 16 | 11 | 31 | 12 | 22 | 8 | 21 | 15 | 9 | 9 | 6 | 6 | 14 | |||||||||
Electricity | 5 | 6 | 6 | 5 | 5 | 5 | 5 | 6 | 9 | 8 | 15 | 15 | 6 | 12 | 6 | 6 | 8 | ||||||
Coupon | 10 | 10 | 10 | ||||||||||||||||||||
Water | 7 | 7 | 6 | 6 | 18 | 6 | 12 | 9 | |||||||||||||||
Air conditioning | 6 | 6 | 5 | 5 | 4 | 15 | 10 | 7 | |||||||||||||||
Office | 7 | 7 | 7 | ||||||||||||||||||||
Consuming | 7 | 7 | 6 | 6 | 7 | ||||||||||||||||||
Water heating | 6 | 6 | 6 | ||||||||||||||||||||
Refrigerator | 3 | 3 | 3 | 3 | 12 | 12 | 6 | ||||||||||||||||
Power inverter | 6 | 6 | 6 | ||||||||||||||||||||
Watt | 5 | 5 | 6 | 6 | 5 | ||||||||||||||||||
Light-emitting diode | 5 | 6 | 6 | 6 | 6 | 6 | 5 | 7 | 6 | 6 | 6 | 3 | 3 | 6 | |||||||||
Efficient energy use | 8 | 5 | 7 | 2 | 2 | 9 | 9 | 6 | 3 | 5 | 3 | 3 | 5 | ||||||||||
Home | 4 | 5 | 5 | 6 | 6 | 5 |
Kind of Webpage | Educational | Commercial | Non-Profit Organization | Media | Government | Wikipedia |
---|---|---|---|---|---|---|
Canada | 20% | 70% | 0% | 0% | 10% | 0% |
USA | 30% | 40% | 0% | 0% | 30% | 0% |
North America | 25% | 55% | 0% | 0% | 20% | 0% |
Ireland | 20% | 50% | 0% | 10% | 20% | 0% |
Spain | 40% | 10% | 20% | 0% | 30% | 0% |
UK | 20% | 60% | 0% | 10% | 10% | 0% |
Europe | 27% | 40% | 6% | 7% | 20% | 0% |
Australia | 20% | 40% | 0% | 0% | 30% | 10% |
New Zealand | 20% | 50% | 0% | 0% | 30% | 0% |
Oceania | 20% | 45% | 0% | 0% | 30% | 5% |
Hong Kong | 20% | 10% | 0% | 0% | 60% | 10% |
India | 80% | 10% | 0% | 0% | 10% | 0% |
Singapore | 20% | 40% | 0% | 20% | 20% | 0% |
Asia | 40% | 20% | 0% | 7% | 30% | 3% |
Argentina | 40% | 20% | 10% | 30% | 0% | 0% |
Chile | 20% | 40% | 20% | 20% | 0% | 0% |
Colombia | 40% | 30% | 10% | 10% | 10% | 0% |
Mexico | 30% | 20% | 10% | 10% | 30% | 0% |
Peru | 60% | 0% | 0% | 20% | 20% | 0% |
Venezuela | 30% | 10% | 0% | 40% | 20% | 0% |
Latin America | 38% | 20% | 7% | 22% | 13% | 0% |
South Africa | 30% | 40% | 0% | 0% | 10% | 20% |
Africa | 30% | 40% | 0% | 0% | 10% | 20% |
USA | Canada | North América | Ireland | Spain | UK | Europe | Australia | New Zealand | Oceania | Hong Kong | India | Singapore | Asia | Argentina | Chile | Colombia | Mexico | Peru | Venezuela | Latin America | South Africa | Africa | Global | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Electronic Devices | 18% | 8% | 13% | 40% | 38% | 16% | 31% | 20% | 10% | 15% | 14% | 26% | 8% | 16% | 14% | 14% | 32% | 16% | 28% | 18% | 20% | 12% | 12% | 18% |
Human | 14% | 20% | 17% | 30% | 18% | 14% | 21% | 22% | 14% | 18% | 24% | 18% | 14% | 19% | 14% | 8% | 22% | 20% | 58% | 20% | 24% | 14% | 14% | 19% |
Nature | 66% | 42% | 54% | 56% | 52% | 48% | 52% | 42% | 28% | 35% | 38% | 42% | 26% | 35% | 46% | 34% | 36% | 62% | 58% | 52% | 48% | 30% | 30% | 42% |
Infographics | 12% | 12% | 12% | 34% | 10% | 6% | 17% | 8% | 14% | 11% | 22% | 10% | 6% | 13% | 30% | 22% | 34% | 52% | 12% | 8% | 26% | 2% | 2% | 13% |
Products | 2% | 20% | 11% | 16% | 0% | 18% | 11% | 12% | 20% | 16% | 22% | 14% | 26% | 21% | 18% | 24% | 4% | 22% | 2% | 20% | 15% | 24% | 24% | 16% |
Money | 12% | 12% | 12% | 6% | 10% | 4% | 7% | 8% | 2% | 5% | 0% | 0% | 6% | 2% | 8% | 4% | 10% | 10% | 6% | 4% | 7% | 6% | 6% | 6% |
Illumination | 58% | 66% | 62% | 44% | 50% | 60% | 51% | 60% | 72% | 66% | 58% | 54% | 64% | 59% | 46% | 70% | 78% | 60% | 40% | 52% | 58% | 70% | 70% | 61% |
Home | 12% | 10% | 11% | 28% | 12% | 8% | 16% | 6% | 10% | 8% | 4% | 6% | 4% | 5% | 4% | 4% | 8% | 8% | 12% | 4% | 7% | 10% | 10% | 9% |
Transportation | 2% | 2% | 2% | 2% | 4% | 2% | 3% | 2% | 2% | 2% | 2% | 0% | 2% | 1% | 0% | 0% | 4% | 0% | 8% | 4% | 3% | 0% | 0% | 2% |
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Valerio-Ureña, G.; Rogers, R. Characteristics of the Digital Content about Energy-Saving in Different Countries around the World. Sustainability 2019, 11, 4704. https://doi.org/10.3390/su11174704
Valerio-Ureña G, Rogers R. Characteristics of the Digital Content about Energy-Saving in Different Countries around the World. Sustainability. 2019; 11(17):4704. https://doi.org/10.3390/su11174704
Chicago/Turabian StyleValerio-Ureña, Gabriel, and Richard Rogers. 2019. "Characteristics of the Digital Content about Energy-Saving in Different Countries around the World" Sustainability 11, no. 17: 4704. https://doi.org/10.3390/su11174704
APA StyleValerio-Ureña, G., & Rogers, R. (2019). Characteristics of the Digital Content about Energy-Saving in Different Countries around the World. Sustainability, 11(17), 4704. https://doi.org/10.3390/su11174704