Integrated and effective management of muck waste under the platform governance mode for a circular economy.

As an inevitable part of construction and demolition (C&D) waste, muck has a dreadful environmental impact due its inadequate management by the traditional governance process. This paper therefore focuses on the management of muck generated from C&D waste by utilizing platform governance as an alternative process, which should more effectively contribute to China's circular economy. The study explores the feasibility of providing such a platform governance mode by using Petri net to compare the traditional governance process and platform governance process for the management of muck trucks, and by using Nanjing's muck smart supervision platform as a case study to assess the effectiveness of the platform governance mode. Results from Petri net simulation modeling reveal that the platform governance mode is more effective than the traditional mode, and from the case study it is found that the success of Nanjing's muck waste management can be attributed to the platform governance mode. The platform management approach can therefore contribute to the sustainability of muck waste governance, and is suitable as an integrated and effective management mode for current practices of muck waste management and resource recovery in China. The main finding from the study is that the platform governance mode significantly improves the efficiency of muck waste management as compared with the traditional governance mode and can therefore provide greater economic and environmental benefits as part of a circular economy.

Development of an extended STIRPAT model to assess the driving factors of household carbon dioxide emissions in China.

Although the past twenty years have witnessed China's remarkable economic development, the cost in terms of greenhouse gas emissions and a deteriorating environment has been enormous. Numerous studies have revealed the influence of household factors on household carbon dioxide emissions (HCEs) and called for a reduction of HCEs to mitigate climate change, but few have focused on assessing the most significant household driving factors of HCEs. Using statistical data between 2005 and 2019 in Jiangsu, China, this study developed an extended stochastic impact by regression on population, affluence, and technology (STIRPAT) model to assess the most significant driving factors of HCEs. The results show that the most significant driving factors are household size, total population, unemployment, and urbanisation rate. The study found that HCEs are positively impacted by household size while negatively impacted by the unemployment rate. Based on the study's findings, the following suggestions are proposed to lower HCEs: (i) establish an optimal consumption concept to guide residents towards consuming reasonably; (ii) cultivate a low-carbon concept among residents and promote low-carbon emissions living; and (iii) pay close attention to population structure factors and formulate effective measures accordingly. The study provides insightful information on the key driving factors of HCEs, which can facilitate achieving carbon emissions neutrality.