Evaluation of Portuguese Population’s Perspectives on Chemical Innovations for Sustainable Development-Journal of Sustainability Research-CSCIED科技核心评价数据库-手机版

Evaluation of Portuguese Population’s Perspectives on Chemical Innovations for Sustainable Development

Evaluation of Portuguese Population’s Perspectives on Chemical Innovations for Sustainable Development

ES评分 0 浏览量：299 下载量：0

DOI	10.20900/jsr.20240057
刊名	Journal of Sustainability Research JSR
年，卷(期)	2024, 6(3)
作者	Margarida Figueiredo,Henrique Vicente*,
作者单位	1 Department of Chemistry and Biochemistry, School of Science and Technology & CIEP, University of Évora, Évora 7000-671, Portugal; 2 Department of Chemistry and Biochemistry, School of Science and Technology & REQUIMTE/LAQV, University of Évora, Évora 7000-671, Portugal; 3 Algoritmi Center/LASI, University of Minho, Braga 4710-057, Portugal
Abstract	Background: Chemistry undeniably contributes numerous discoveries and innovations that impact various aspects of societal life and can decisively contribute to more sustainable development. The aim of this study is to evaluate the Portuguese population perspectives on chemical innovations that support sustainable development, focus particularly the topics food industry, health, water technologies, agriculture, energy, and environment. Methods: The study involved the development and validation of a questionnaire and was conducted using a representative sample of the Portuguese population, with 452 participants of both genders whose ages ranged from 15 to 83 years. Results: The outcomes from this research highlight that the role chemical innovations for sustainable development are positively perceived in in all topics included in the study, apart from energy. A relative high portion of participants lack knowledge regarding recent chemical developments. A global analysis of the results shows that these perspectives are similar for both genders, independent of residential area, but higher among individuals aged 26 to 65 years and those with higher academic qualifications. Additionally, a model based on Artificial Neural Networks was presented to predict the perspectives of the Portuguese population regarding the chemical innovations for sustainable development. The proposed model performs well, achieving accuracy rates higher than 90%. Conclusions: The study introduces a new method to evaluate the Portuguese population’s overall perspective on chemical innovations for sustainable development and its capacity for improvement. This evaluation is crucial for planning strategies to promote public awareness of the role of chemical innovations in this context.
KeyWord	public perspectives evaluation; chemical innovations; sustainable development; artificial neural networks
基金项目
页码	-

参考文献
相关文献

1.United Nations General Assembly. Transforming Our World: The 2030 Agenda for Sustainable Development. Available from: https://www.refworld.org/legal/resolution/unga/2015/en/111816. Accessed on 10 Jul 2024. 2. United Nations, Department of Economic and Social Affairs, Population Division. World Population Prospects 2022: Summary of Results. Available from: https://www.un.org/development/desa/pd/sites/www.un.org.development.desa.pd/files/undesa_pd_2022_wpp_key-messages.pdf. Accessed on 10 Jul 2024. 3. Irewale AT, Dimkpa CO, Agunbiade FO, Oyetunde OA, Elemike EE, Oguzie EE. Unlocking Sustainable Agricultural Development in Africa via Bio-Nanofertilizer Application—Challenges, Opportunities and Prospects. Sci Afr. 2024;25:e02276. 4. Ashraf SA, Siddiqui AJ, Elkhalifa AEO, Khan MI, Patel M, Alreshidi M, et al. Innovations in nanoscience for the sustainable development of food and agriculture with implications on health and environment. Sci Total Environ. 2021:768:144990. 5. Kumar A, Choudhary A, Kaur H, Mehta S, Husen A. Smart Nanomaterial and Nanocomposite with Advanced Agrochemical Activities. Nanoscale Res Lett. 2021;16:156. 6. Mishra SR, Gadore V, Ahmaruzzaman M. From light to chemicals: Breaking ground in photocatalytic H2O2 production for a sustainable future. Mater Today Sustain. 2024;27:100819. 7. Molaiyan P, Bhattacharyya S, Reis GS, Sliz R, Paolella A, Lassi U. Towards greener batteries: sustainable components and materials for next-generation batteries. Green Chem. 2024;26:7508-31. 8. Anagnostopoulou C, Papachristou I, Kyriakoudi A, Kontogiannopoulos KN, Mourtzinos I, Kougias PG. Development of alginate beads loaded with bioactive ingredients from Chlorella vulgaris cultivated in food industry wastewaters. Algal Res. 2024;80:103530. 9. Lodhi A, Maheria KC. Zeolite-catalysed esterification of biomass-derived acids into high-value esters products: Towards sustainable chemistry. Catal Commun. 2024;187:106883. 10. Stancu EC, Ionita MD, Quade A, Ionita ER. Surface properties and antibacterial characteristics of polyurethane modified by corona discharge for food processing industry. Innov Food Sci Emerg Technol. 2024;95:103729. 11. Hassoun A, Boukid F, Ozogul F, Aït-Kaddour A, Soriano JM, Lorenzo JM, et al. Creating new opportunities for sustainable food packaging through dimensions of industry 4.0: New insights into the food waste perspective. Trends Food Sci Technol. 2023;142:104238. 12. Felipe LO, Oliveira AM, Bicas, JL. Bioaromas—Perspectives for sustainable development. Trends Food Sci Technol. 2017;62:141-53. 13. Gupta V, Jamwal G, Rai GK, Gupta SK, Shukla RM, Dadrwal BK, et al. Biosynthesis of biomolecules from saffron as an industrial crop and their regulation, with emphasis on the chemistry, extraction methods, identification techniques, and potential applications in human health and food: A critical comprehensive review. Biocatal Agric Biotechnol. 2024;59:103260. 14. Souto EB, Blanco-Llamero C, Krambeck K, Kiran NS, Yashaswini C, Postwala H, et al. Regulatory insights into nanomedicine and gene vaccine innovation: Safety assessment, challenges, and regulatory perspectives. Acta Biomater. 2024;180:1-17. 15. Naeem A, Saeed B, AlMohamadi H, Lee M, Gilani MA, Nawaz R, et al. Sustainable and green membranes for chemical separations: A review. Sep Purif Technol. 2024;336:126271. 16. Screpanti C. Chemical Innovation and Agrifood Systems in Switzerland: A Short Perspective of the Sustainable Development Goals. Chimia. 2024;78(6):390-6. 17. Duarah P, Haldar D, Patel AK, Dong CD, Singhania RR, Purkait MK. A review on global perspectives of sustainable development in bioenergy generation. Bioresour Technol. 2022;348:126791. 18. Zhong J, Kan HY. The impact of government policy, natural resources and ecological innovations on energy transition and environmental sustainability: Insights from China. Resour Policy. 2024;89:104531. 19. Zuin VG, Eilks I, Elschami M, Kümmerer K. Education in green chemistry and in sustainable chemistry: perspectives towards sustainability. Green Chem. 2021;23:1594-608. 20. Chen TL, Kim H, Pan SY, Tseng PC, Lin YP, Chiang PC. Implementation of green chemistry principles in circular economy system towards sustainable development goals: Challenges and perspectives. Sci Total Environ. 2020;716:136998. 21. Loste N, Chinarro D, Gomez M, Roldán E, Giner B. Assessing awareness of green chemistry as a tool for advancing sustainability. J Clean Prod. 2020;256:120392. 22. Barra R, González P. Sustainable chemistry challenges from a developing country perspective: Education, plastic pollution, and beyond. Curr Opin Green Sustain Chem. 2018;9:40-4. 23. Mehta G, Cornell SE, Krief A, Hopf H, Matlin SA. A shared future: chemistry’s engagement is essential for resilience of people and planet. R Soc Open Sci. 2022;9:212004. 24. Guerris M, Cuadros J, González-Sabaté L, Serrano V. Describing the public perception of chemistry on Twitter. Chem Educ Res Pract. 2020;21(3):989-99. 25. Dobbelaar E, Richter J. An overview of young chemists’ expectations towards the sustainable development of the chemical sector. Opinions that matter. Pure Appl Chem. 2022;94(1):1-14. 26. Ferreira BM, Abrantes JL, Reis M, Brambilla FR. A longitudinal study on sustainability perceptions in Portugal. Sustainability. 2023;15(7):5893. 27. de Waard EF, Prins GT, van Joolingen WR. Pre-university students’ perceptions about the life cycle of bioplastics and fossil-based plastics. Chem Educ Res Pract. 2020;21(3):908-21. 28. Witten IH, Frank E, Hall MA, Pal CJ. Data Mining—Practical Machine Learning Tools and Techniques. 4th ed. Cambridge (US): Morgan Kaufmann; 2017. 29. Haykin S. Neural Networks and Learning Machines. 3rd ed. New York (US): Prentice Hall; 2009. 30. Rumelhart D, Hinton G, Williams R. Learning Internal Representation by Error Propagation. In: Rumelhart DE, McCleland JL. editors. Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations. Massachusetts (US): MIT Press; 1987. p. 318-62. 31. Alkhatib K, Khazaleh H, Alkhazaleh HA, Alsoud AR, Abualigah LA. New Stock Price Forecasting Method Using Active Deep Learning Approach. J Open Innov Technol Mark Complex. 2022;8:96. 32. Chhajer P, Shah M, Kshirsagar A. The applications of artificial neural networks, support vector machines, and long–short term memory for stock market prediction. Decis Anal J. 2022;2:100015. 33. Alshayeji M, Ellethy H, Abed S, Gupta R. Computer-aided detection of breast cancer on the Wisconsin dataset: An artificial neural networks approach. Biomed. Signal Process Control. 2022;71:103141. 34. Li A, Qi M, Li W, Yu X, Yang L, Wang J, et al. Prediction and verification of the effect of psoriasis on coronary heart disease based on artificial neural network. Heliyon. 2022;8:e10677. 35. Tumpa P, Kabir MA. An artificial neural network based detection and classification of melanoma skin cancer using hybrid texture features. Sens Int. 2021;2:100128. 36. Salgado C, Dam R, Puertas E, Salgado W. Calculation of volume fractions regardless scale deposition in the oil industry pipelines using feed-forward multilayer perceptron artificial neural network and MCNP6 code. Appl Radiat Isot. 2022;185:110215. 37. Batista L, Marques C, Pires A, Minim L, Soares N, Vidigal M. Artificial neural networks modeling of non-fat yogurt texture properties: effect of process conditions and food composition. Food Bioprod Process. 2021;126:164-74. 38. Li C, Zhang C, Yu T, Liu X, Yang Y, Hou Q, et al. Use of artificial neural network to evaluate cadmium contamination in farmland soils in a karst area with naturally high background values. Environ Pollut. 2022;304:119234. 39. Dawood T, Elwakil E, Hector H, Delgado J. Toward urban sustainability and clean potable water: Prediction of water quality via artificial neural networks. J Clean Prod. 2021;291:125266. 40. Goudarzi G, Hopke P, Yazdani M. Forecasting PM2.5 concentration using artificial neural network and its health effects in Ahvaz, Iran. Chemosphere. 2021;283:131285. 41. Fernandes A, Chaves H, Lima R, Neves J, Vicente H. Draw on artificial neural networks to assess and predict water quality. IOP Conf Ser Earth Environ Sci. 2020;612:012028. 42. Bishop CM, Bishop H. Deep Learning—Foundations and Concepts. 1st ed. Cham (Switzerland): Springer; 2024. 43. Aggarwal C. Neural Networks and Deep Learning: A Textbook. 2nd ed. Cham (Switzerland): Springer; 2023. 44. Fernandes A, Vicente H, Figueiredo M, Neves M, Neves J. An evaluative model to assess the organizational efficiency in training corporations. In: Dang T, Wagner R, Küng J, Thoai N, Takizawa M, Neuhold E, editors. Future Data and Security Engineering, Lecture Notes in Computer Science. Cham (Switzerland): Springer; 2016. p. 415-28. 45. Cohen L, Manion L, Morrison K. Research Methods in Education. 8th ed. New York (US): Routledge; 2017. 46. DeKetele JM, Roegiers X. Méthodologie du recueil d’informations: Fondements des méthodes d'observation, de questionnaire, d’interview et d’étude de documents
[Information Collection Methodology: Foundations of observation methods, questionnaires, interviews, and document studies]. 5th ed. Paris (France): DeBoeck Universite; 2016. French. 47. Patton MQ. Qualitative Research and Evaluation Methods: Integrating Theory and Practice. 4th ed. Thousand Oaks (US): SAGE Publications Inc; 2015. 48. McMillan J, Schumacher S. Research in Education: Evidence-Based Inquiry. 7th ed. New York (US): Prentice Hall; 2009. 49. Bell J. Doing your research project: A guide for first-time researchers in education, health and social science. 5th ed. Maidenhead (UK): Open University Press; 2010. 50. Frank E, Hall M, Witten IH. The WEKA workbench—Online appendix for “data mining: practical machine learning tools and techniques” Morgan Kaufmann, fourth edition, 2016. Available from: https://www.cs.waikato.ac.nz/ml/weka/Witten_et_al_2016_appendix.pdf. Accessed on 10 Jul 2024. 51. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH. The WEKA data mining software: an update. SIGKDD Explor. 2009;11:10-8. doi: 10.1145/1656274.1656278 52. Kewley R, Embrechts M, Breneman C. Data strip mining for the virtual design of pharmaceuticals with neural networks. IEEE Trans Neural Netw. 2000;11:668-79.

引用本文

Margarida Figueiredo,Henrique Vicente*. Evaluation of Portuguese Population’s Perspectives on Chemical Innovations for Sustainable Development, Journal of Sustainability Research. 2024; 6; (3). https://doi.org/10.20900/jsr.20240057.

文献评论

相关学者

相关机构