- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
Current attempts for sustainable-focused smart community evaluation have failed to make significant advancements, and quantitative analysis for sustainable development is still a major challenge in China. In recent years, smart community evaluation (SCE) for sustainable development has attracted considerable attentions. Government decision-makers can make it easier to stimulate household sustainable consumption by conducting SCE. This paper develops a combined analytical framework that will assist in the process of multi-source data integration and uncertain reasoning of SCE. This framework is used to combine quantitative metrics and subjective judgment with evidential reasoning approach, and this frarmework can also take decision makers’ risk preferences into consideration using prospect theory. Four urban communities are evaluated by the proposed framework to demonstrate its applicability and effectiveness.
In this paper, we propose a combined framework for sustainable-focused smart community evaluation. First, compared with traditional methods, the developed transformation technique employs prospect value to enhance the performance of multi-source data integration. Second, this analytical framework can be applied to evaluation problems without knowing the evaluation criteria in advance. In addition, this study focused on risk preference and demonstrated the impact of that on the smart community evaluation. Based on the above experiment analysis and conclusions, we derive the following policy implications: first, for the rapid and sustainable development of smart community, we should make more efforts on the construction of mandatory indicators in the present stage. Due to limited resources and funds, it is significant to make appropriate allocation of them on mandatory indicators according to our proposed evaluation indicator system. Thus, construction in order is a good way to not waste resources and funds on meaningless pursuits. Second, evaluation results are affected by risk attitudes of decision maker, so that government should take these into consideration and adjust the main emphasis of smart community construction timely. In addition, we should make full use of the government's guiding role in policy making, and encourage more and more participants to make efforts for smart community construction. In the real world, it worth to note that combined smart community evaluation always involves incomplete information of indicators. In other words, if the incompleteness of indicators is considered in this paper, that is to say , the analytical framework is not applicable. In future work, we are m?,? ≠ 0 planning to investigate a more feasible approach for ranking smart communities considering the incompleteness of each indicator.