2. State of the Art
2.2. Resource Efficiency
2.3. Sustainable Energy
3. Survey Characteristics
- The questionnaire was initially developed in English and then translated into Chinese by the local partners from the Institute of Applied Ecology (Chinese Academy of Sciences, Shenyang, China). For a pretest, the questionnaire was discussed with some potential interviewees and slightly revised based on their feedback and comments. To verify the translation, the Chinese questionnaire was re-translated again into German by a native Chinese Speaker, who is not involved in the project.
- The distribution of the 120 questionnaires was carried out via email and through on-site visits including direct interviews. Additionally, the distribution was supported by the local government. All of the 120 sent out questionnaires were returned; most were sent back via email (47) or were collected from the local governments (40), while 22 were collected on site.
- Finally, the collected responses were aggregated and documented. Of the 120 questionnaires that were sent out, 11 were returned incomplete, leading to a sample of 109 complete questionnaires. The sample focused on medium to large sized companies. Most are located in Liaoning Province (typical industrial zone located in northeastern China) with 102 returned questionnaires; seven questionnaires were collected from Jiangsu Province and Gansu Province. Fifty-six percent of the companies involved in the survey have more than 250 but less than 1000 employees, 22% employ between 1000 and 2500 people, 6% fewer than 5000 but more than 2500 people and 16% more than 5000 people. The main branches represented in the sample are machine and plant engineering (24%), automotive (22%), information and communication technologies (17%), electronics (15%) and aerospace (12%). Participants in the survey are mainly male (73.4%) and work in the two engineering domains: development (34%) and manufacturing (66%). The sample choice (regional focus, focus on medium and large size companies) was based on the experience and networks of our Chinese partners to increase the probability for good availability and relevance of partners.
4. Survey Results
4.2. Resource Efficiency
4.3. Sustainable Energy
Conflicts of Interest
- United Nations Environment Programme. Towards a Green Economy: Pathways to Sustainable Development and Poverty Eradication; UNEP: Nairobi, Kenya, 2011. [Google Scholar]
- UN General Assembly. Transforming our World: The 2030 Agenda for Sustainable Development; UN General Assembly: New York, NY, USA, 2015; Available online: https://sustainabledevelopment.un.org/post2015/transformingourworld (accessed on 21 November 2017).
- National Bureau of Statistics of China. China Statistical Yearbook 2016: 3-6 Value-added by Sector. Available online: http://www.stats.gov.cn/tjsj/ndsj/2016/indexeh.htm (accessed on 11 December 2017).
- Wübbeke, J.; Meissner, M.; Zenglein, M.J.; Ives, J.; Conrad, B. MADE IN CHINA 2025. The Making of a High-Tech Superpower and Consequences for Industrial Countries; Mercator Institute for China Studies: Berlin, Germany, 2016; Available online: https://www.merics.org/sites/default/files/2017-09/MPOC_No.2_MadeinChina2025.pdf (accessed on 11 December 2017).
- National Bureau of Statistics of China. China Statistical Yearbook 2016: 9-3 Overall Energy Balance Sheet. Available online: http://www.stats.gov.cn/tjsj/ndsj/2016/indexeh.htm (accessed on 11 December 2017).
- National Bureau of Statistics of China. China Statistical Yearbook 2016: 9-2 Total Consumption of Energy and Its Composition. Available online: http://www.stats.gov.cn/tjsj/ndsj/2016/indexeh.htm (accessed on 21 December 2017).
- Ma, Q.; Cai, S.; Wang, S.; Zhao, B.; Martin, R.V.; Brauer, M.; Cohen, A.; Jiang, J.; Zhou, W.; Hao, J.; et al. Impacts of coal burning on ambient PM2.5 pollution in China. Atmos. Chem. Phys. 2017, 17, 4477–4491. [Google Scholar] [CrossRef]
- National Bureau of Statistics of China. China Statistical Yearbook 2015: 8-19 Ambient Air Quality in Key Cities of Environmental Protection (2014). Available online: http://www.stats.gov.cn/tjsj/ndsj/2015/indexeh.htm (accessed on 11 December 2017).
- World Health Organization Regional Office for Europe. WHO Expert Consultation: Available Evidence for the Future Update of the WHO Global Air Quality Guidelines (AQGs); WHO: Geneva, Switzerland, 2016. [Google Scholar]
- Ministry of Environmental Protection the People’s Republic of China. Pollution Curbs Set to Make Skies Clearer; Ministry of Environmental Protection the People’s Republic of China: Beijing, China, 2017.
- Niehoff, S.; Beier, G. Industrie 4.0 and a sustainable development: A short study on the perception and expectations of experts in Germany. Int. J. Innov. Sustain. Dev. in press.
- Pineiro-Chousa, J.; Vizcaíno-González, M.; López-Cabarcos, M.; Romero-Castro, N. Managing Reputational Risk through Environmental Management and Reporting: An Options Theory Approach. Sustainability 2017, 9, 376. [Google Scholar] [CrossRef]
- Keeso, A. Big Data and Environmental Sustainability: A Conversation Starter; Smith School of Enterprise and the Environment: Oxford, UK, 2015. [Google Scholar]
- Song, M.-L.; Fisher, R.; Wang, J.-L.; Cui, L.-B. Environmental performance evaluation with big data: Theories and methods. Ann. Oper. Res. 2016, 39, 1261. [Google Scholar] [CrossRef]
- Jayal, A.D.; Badurdeen, F.; Dillon, O.W.; Jawahir, I.S. Sustainable manufacturing: Modeling and optimization challenges at the product, process and system levels. CIRP J. Manuf. Sci. Technol. 2010, 2, 144–152. [Google Scholar] [CrossRef]
- Duflou, J.R.; Sutherland, J.W.; Dornfeld, D.; Herrmann, C.; Jeswiet, J.; Kara, S.; Hauschild, M.; Kellens, K. Towards energy and resource efficient manufacturing: A processes and systems approach. CIRP Ann.-Manuf. Technol. 2012, 61, 587–609. [Google Scholar] [CrossRef]
- Brecher, C.; Herfs, W.; Heyers, C.; Klein, W.; Triebs, J.; Beck, E.; Dorn, T. Ressourceneffizienz von Werkzeugmaschinen im Fokus der Forschung: Effizienzsteigerung durch Optimierung der Technologien zum Komponentenbetrieb. Werkstattstech. Online 2010, 100, 559–564. [Google Scholar]
- Gu, C.; Leveneur, S.; Estel, L.; Yassine, A. Modeling and Optimization of Material/Energy Flow Exchanges in an Eco-Industrial Park. Energy Procedia 2013, 36, 243–252. [Google Scholar] [CrossRef]
- Rohn, H.; Pastewski, N.; Lettenmeier, M.; Wiesen, K.; Bienge, K. Resource efficiency potential of selected technologies, products and strategies. Sci. Total Environ. 2014, 473, 32–35. [Google Scholar] [CrossRef] [PubMed]
- Ford, S.; Despeisse, M. Additive manufacturing and sustainability: An exploratory study of the advantages and challenges. J. Clean. Prod. 2016, 137, 1573–1587. [Google Scholar] [CrossRef]
- Song, R.; Sun, X.; Zheng, Y.; Hu, H.; Lie, J. Application and Prospection of Internet of Things Technology in Waste Management. Appl. Mech. Mater. 2015, 768, 797–803. [Google Scholar] [CrossRef]
- Chukwuekwe, D.O. Condition Monitoring for Predictive Maintenance: A Tool for Systems Prognosis within the Industrial Internet Applications; NTNU (Norwegian University of Science and Technology): Trondheim, Norway, 2016. [Google Scholar]
- Reid, M.; Cook, B. The Application of Smart, Connected Power Plant Assets for Enhanced Condition Monitoring and Improving Equipment Reliability. In Proceedings of the ASME 2016 Power Conference Collocated with the ASME 2016 10th International Conference on Energy Sustainability and the ASME 2016 14th International Conference on Fuel Cell Science, Engineering and Technology, Charlotte, CA, USA, 26–30 June 2016. [Google Scholar]
- Global e-Sustainability Initiative. Smarter 2030. ICT Solutions for 21st Century Challenges; Global e-Sustainability Initiative: Brüssel, Belgium, 2015. [Google Scholar]
- Hilty, L.M.; Aebischer, B.; Rizzoli, A.E. Modeling and evaluating the sustainability of smart solutions. Environ. Model. Softw. 2014, 1–5. [Google Scholar] [CrossRef]
- Energieeffizienz in der Produktion. Untersuchung zum Handlungs- und Forschungsbedarf. Available online: https://www.fraunhofer.de/content/dam/zv/de/forschungsthemen/energie/Studie_Energieeffizienz-in-der-Produktion.pdf (accessed on 11 November 2017).
- Fysikopoulos, A.; Pastras, G.; Alexopoulos, T.; Chryssolouris, G. On a generalized approach to manufacturing energy efficiency. Int. J. Adv. Manuf. Technol. 2014, 73, 1437–1452. [Google Scholar] [CrossRef]
- Brizzi, P.; Conzon, D.; Khaleel, H.; Tomasi, R.; Pastrone, C.; Spirito, A.M.; Knechtel, M.; Pramudianto, F.; Cultrona, P. Bringing the Internet of Things along the manufacturing line: A case study in controlling industrial robot and monitoring energy consumption remotely. In Proceedings of the 2013 IEEE 18th Conference on Emerging Technologies & Factory Automation (ETFA), Cagliari, Italy, 10–13 September 2013; pp. 1–8. [Google Scholar]
- Lennartson, B.; Bengtsson, K. Smooth robot movements reduce energy consumption by up to 30 percent. Eur. Energy Innov. 2016, Spring 2016, 38. [Google Scholar]
- Riazi, S.; Bengtsson, K.; Bischoff, R.; Aurnhammer, A.; Wigstrom, O.; Lennartson, B. Energy and peak-power optimization of existing time-optimal robot trajectories. In Proceedings of the 2016 IEEE International Conference on Automation Science and Engineering (CASE), Fort Worth, TX, USA, 21–25 August 2016; pp. 321–327. [Google Scholar]
- Thiede, S.; Seow, Y.; Andersson, J.; Johansson, B. Environmental aspects in manufacturing system modelling and simulation—State of the art and research perspectives. CIRP J. Manuf. Sci. Technol. 2013, 6, 78–87. [Google Scholar] [CrossRef]
- Herrmann, C.; Thiede, S.; Kara, S.; Hesselbach, J. Energy oriented simulation of manufacturing systems—Concept and application. CIRP Ann.-Manuf. Technol. 2011, 60, 45–48. [Google Scholar] [CrossRef]
- Zhao, W.-B.; Jeong, J.-W.; Noh, S.D.; Yee, J.T. Energy simulation framework integrated with green manufacturing-enabled PLM information model. Int. J. Precis. Eng. Manuf.-Green Technol. 2015, 2, 217–224. [Google Scholar] [CrossRef]
- Weinert, N.; Chiotellis, S.; Seliger, G. Methodology for planning and operating energy-efficient production systems. CIRP Ann.-Manuf. Technol. 2011, 60, 41–44. [Google Scholar] [CrossRef]
- Caggiano, A.; Marzano, A.; Teti, R. Sustainability Enhancement of a Turbine Vane Manufacturing Cell through Digital Simulation-Based Design. Energies 2016, 9, 790. [Google Scholar] [CrossRef]
- Garwood, T.L.; Hughes, B.R.; Oates, M.R.; O’Connor, D.; Hughes, R. A review of energy simulation tools for the manufacturing sector. Renew. Sustain. Energy Rev. 2018, 81, 895–911. [Google Scholar] [CrossRef]
- Shabanzadeh, M.; Sheikh-El-Eslami, M.-K.; Haghifam, M.-R. The design of a riskhedging tool for virtual power plants via robust optimization approach. Appl. Energy 2015, 155, 766–777. [Google Scholar] [CrossRef]
- Bornschlegl, M.; Drechsel, M.; Kreitlein, S.; Bregulla, M.; Franke, J. A new approach to increasing energy efficiency by utilizing cyber-physical energy systems. In Proceedings of the 11th Workshop on Intelligent Solutions in Embedded Systems (WISES), Pilsen, Czech Republic, 10–11 September 2013. [Google Scholar]
- Schmidt, C.; Li, W.; Thiede, S.; Kara, S.; Herrmann, C. A methodology for customized prediction of energy consumption in manufacturing industries. Int. J. Precis. Eng. Manuf.-Green Technol. 2015, 2, 163–172. [Google Scholar] [CrossRef]
- International Renewable Energy Agency. Innovation Outlook: Renewable Mini-Grids, 2016; International Renewable Energy Agency: Abu Dhabi, United Arab Emirates, 2016; Available online: http://www.irena.org/DocumentDownloads/Publications/IRENA_Innovation_Outlook_Minigrids_2016.pdf (accessed on 8 December 2017).
- Crosby, M.; Pattanayak, P.; Verma, S.; Kalyanaraman, V. Blockchain technology: Beyond bitcoin. Appl. Innov. Rev. 2016, 6–19. [Google Scholar]
- Africa Progress Panel. Lights Power Action; Africa Progress Panel: Geneva, Switzerland, 2017; Available online: http://www.africaprogresspanel.org/wp-content/uploads/2017/04/APP_Lights_Power_Action_ Web_PDF_Final.pdf (accessed on 8 December 2017).
- Vorläufige Umsetzungsempfehlungen für das Zukunftsprojekt Industrie 4.0. Deutschlands Zukunft als Produktionsstandort Sichern. Available online: https://www.bmbf.de/files/Umsetzungsempfehlungen_Industrie4_0.pdf (accessed on 8 December 2017).
- Organisation for Economic Co-Operation and Development (OECD). Renewable Energy (Indicator); OECD: Paris, France, 2017; Available online: https://data.oecd.org/energy/renewable-energy.htm (accessed on 13 December 2017).
- Beier, G.; Niehoff, S.; Ziems, T.; Xue, B. Sustainability aspects of a digitalized industry—A comparative study from China and Germany. Int. J. Precis. Eng. Manuf.-Green Technol. 2017, 4, 227–234. [Google Scholar] [CrossRef]
- International Renewable Energy Agency (IRENA). Renewable Energy Statistics 2017; International Renewable Energy Agency: Abu Dhabi, United Arab Emirates, 2017. [Google Scholar]
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