LEADER 04780nam 22005055 450 001 9910254866303321 005 20200630054119.0 010 $a3-319-18636-1 024 7 $a10.1007/978-3-319-18636-8 035 $a(CKB)3710000000467343 035 $a(EBL)4085617 035 $a(SSID)ssj0001546937 035 $a(PQKBManifestationID)16141452 035 $a(PQKBTitleCode)TC0001546937 035 $a(PQKBWorkID)14796563 035 $a(PQKB)11252662 035 $a(DE-He213)978-3-319-18636-8 035 $a(MiAaPQ)EBC4085617 035 $a(PPN)188456945 035 $a(EXLCZ)993710000000467343 100 $a20150826d2016 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aTheory and Practices on Innovating for Sustainable Development /$fby Yoram Krozer 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (201 p.) 300 $aDescription based upon print version of record. 311 $a3-319-18635-3 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aIntroduction -- Income Growth and Environmental Impacts -- Markets of Sustainable Innovations -- User Innovations in Solar Power -- Tacit Inventors in Regions -- Arts for Environmental Qualities -- Technology Suppliers for Sanitation -- Alternatives for Commuting -- Ethical Consumers and Producers -- Sustainable Investors and Innovators -- Energy Services for Smart Grid -- Renewable Energy Business and Policy -- Conclusions on Sustainable Innovations -- Index. 330 $aThis book explains how income growth and better environmental qualities go hand in hand, and reviews the drivers and barriers to sustainable innovation on the basis of real-life cases. It discusses why innovation-based income growth reduces environmental impacts and how the huge global markets for sustainable innovation are currently hampered by protectionist policies. Subsequently, diverse sustainable innovators are presented in ascending order of the complexity of interactions between innovators and stakeholders. In this context, innovating consumers who create communities of peers in solar powered mobility are examined in the first case. It also focuses on regional tacit inventors, who spur innovation in tourism mobility thanks to the informal policies but whose efforts are obstructed by the formalities of the European Union. Artists with an interest in both innovations and the environment develop art services that deliver experiences of environmental qualities. Though these experiences have gone unrecognised so far, they are nonetheless socially beneficial. The book also shows how technology suppliers develop four different patterns of sanitation, each with its own pros and cons for the specific community?s needs and conditions. It discusses how project developers also make innovations in office systems, including socially beneficial ones that do away with the need to commute. It includes an analysis of interactions between consumers pursuing ethical consumption and international trailblazers in corporate responsibility and concludes that it is more rewarding to support social entrepreneurs than to attempt to moralise consumers. Further analysis of interactions between sustainable investors and innovators reveals different groups? opinions about policies and markets, helping to explains their weak influence on policies. Mushrooming local energy initiatives are now evolving into energy service companies, producing shifts on energy markets. Why these innovators emerge and how certain policies are blocking them are explained. The policies of the United States and European Union are compared with regard to the main barriers to, and drivers of, the renewable energy business. Though the US invests more money and takes more risks, it is less cost-effective in terms of the number of enterprises and jobs, thanks to the feed-in tariffs in Europe. Lastly, the book also discusses policies that invest in education, skills, knowledge and know-how exchange, but instead abolish perverse subventions of vested interests to create conditions for sustainable development. 606 $aEnvironmental economics 606 $aEnvironmental Economics$3https://scigraph.springernature.com/ontologies/product-market-codes/W48000 615 0$aEnvironmental economics. 615 14$aEnvironmental Economics. 676 $a330 700 $aKrozer$b Yoram$4aut$4http://id.loc.gov/vocabulary/relators/aut$0906128 906 $aBOOK 912 $a9910254866303321 996 $aTheory and Practices on Innovating for Sustainable Development$92026673 997 $aUNINA LEADER 02337oam 2200661M 450 001 9910524885703321 005 20241120175442.0 010 $a0-262-34605-2 024 3 $a9780262346047 035 $a(CKB)4330000002049883 035 $a(CaBNVSL)mat08327692 035 $a(IDAMS)0b00006487b95bf9 035 $a(IEEE)8327692 035 $a(OCoLC)1042908181$z(OCoLC)1088994598 035 $a(OCoLC-P)1042908181 035 $a(MaCbMITP)10654 035 $a(OCoLC)1042908181 035 $a(MdBmJHUP)muse70605 035 $a(MiAaPQ)EBC5340082 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/78554 035 $a(PPN)254843506 035 $a(oapen)doab78554 035 $a(EXLCZ)994330000002049883 100 $a20180605d2018 uy 0 101 0 $aeng 135 $aur|n||||||||| 181 $2rdacontent 182 $2isbdmedia 183 $2rdacarrier 200 10$aMachine learning for data streams $ewith practical examples in MOA /$fAlbert Bifet, Ricard Gavalda?, Geoff Holmes, Bernhard Pfahringer 210 $aCambridge$cThe MIT Press$d2018 210 2$a[Piscataqay, New Jersey] :$cIEEE Xplore,$d[2018] 210 1$aCambridge, Massachusetts ; London, England$cThe MIT Press$d[2017] 215 $a1 PDF (xxi, 262 pages) $cillustrations 225 1 $aAdaptive computation and machine learning 311 08$a0-262-03779-3 311 08$a0-262-34604-4 320 $aIncludes bibliographical references and index. 330 $aA hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework. 410 0$aAdaptive computation and machine learning 606 $aDatabase management 606 $aNeural networks (Computer science) 606 $aMachine learning 615 0$aDatabase management. 615 0$aNeural networks (Computer science) 615 0$aMachine learning. 676 $a006.3/12 700 $aBifet$b Albert$01167471 702 $aGavalda?$b Ricard$f1964- 702 $aHolmes$b Geoff 702 $aPfahringer$b Bernhard 712 02$aMIT Press, 801 0$bOCoLC-P 801 1$bOCoLC-P 906 $aBOOK 912 $a9910524885703321 996 $aMachine Learning for Data Streams$92719505 997 $aUNINA