Big Data Science and Analytics-Based Persuasive Evidence-Synthesis Framework for Sustainable Policymaking in Eco-City Development

The eco-city concept has received widespread support from governments worldwide as a viable solution to pressing sustainable development problems. Eco-city would result in livable and environmentally sustainable communities without resources being wasted or damaging biodiversity. Significant changes in the environmentally acceptable are occurring because of the paradigm change in technology and science brought about by big data research. The planning and design of sustainable cities, as well as their operational management and the use of distributed design approaches and technological solutions, need meticulous attention to detail and the treatment of the whole system. This emerging industry addresses a significant need for bridging the gap between sustainable urbanism and smart city principles. The field of study known as “data-driven smart ecological sustainability” has expanded greatly during the last several years. This paper proposes a big data science and analytics-based persuasive evidence-synthesis framework (BDSA-PESF) for evaluating and planning eco-city development in urban areas. For cities to achieve their sustainability objectives in the face of rapid growth and shifting planning and management systems, data-driven solutions are essential. Pros and downsides of smart urbanism in the context of enacting environmentally friendly urban changes have been examined in this research, prompting more discussions on the topic in the big data era. Researchers learn that there are similarities and distinctions across the many sustainability-related definitions. The numerical outcomes demonstrate that the recommended technique has a high-performance rate of 93.86, prediction rate of 92.11%, tracking rate of 95.23 %, sustainable management ratio of 93.32 %, efficiency ratio of 93.51 %, probability ratio of 96.3 %, accuracy analysis of 89.9 % and energy consumption ratio of 96.8 % than other existing approaches.