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A good risk warning model is an effective way to guarantee the stable operation of the carbon market and exert its emission reduction function. This paper takes the China's pilot carbon market as the research object and measures the carbon market risk by ARMA-GARCH-VaR. Then, influential factors are selected as non-market characteristics from the perspective of causality and incorporated into the risk warning model, and SMOTE and SMOTEENN are introduced to deal with the unbalanced samples. Results show that (1) The risk fluctuations of Beijing and Shenzhen carbon market are large, and the risk fluctuations of Guangdong carbon market are relatively smooth in the early period and larger in the recent period. (2) Air quality, average daily temperature, CSI 300, power coal, natural gas, European carbon market, exchange rate, and Baidu index are all important factors in the risk volatility of carbon market. (3) The Classification Decision Tree (CDT) has the best performance. CDT has the best performance in Guangdong carbon market, SMOTE-CDT has the best performance in Hubei carbon market, and SMOTEENN-CDT has the best performance in Shanghai carbon market, in which SMOTEENN-CDT has a 76% Recall. The non-equilibrium treatment improves the overall Recall, and the risk warning model after non-equilibrium treatment can avoid missing important risk points. This paper can provide a certain degree of reference for the improvement of carbon trading market and risk management. [Display omitted] • A carbon market warning model is constructed. • The average VaR of the carbon trading markets in Shenzhen and Beijing is larger. • CDT performs better in market risk warning. • The model improves 34.8% in the avoidance of risk compared with other models. [ABSTRACT FROM AUTHOR]

In addition to its important economic value, carbon trading is also an important tool in international politics and diplomacy. Carbon price forecast accuracy has far-reaching implications for economic development and the environment. Few existing studies have been more accurate in predicting carbon prices over longer periods. In this paper, the incremental Gaussian nonlinear trend fuzzy granulation method and the Gaussian nonlinear trend fuzzy granulation method are innovatively proposed to predict carbon prices. This study first converts carbon prices into incremental granulation time series. A time-varying core line is added to produce nonlinear trend granulation on the foundation of linear trend granulation. The nonlinear trend and residual information are then predicted using the DeeAR network, respectively, and the final prediction result is obtained by adding the predicted values for each. Moreover, a new evaluation index, the comprehensive evaluation of RMSE, MAE, and MAPE as three indicators, is proposed to consider the accuracy of the evaluation index more comprehensively and reliably. The results show that the prediction method has the smallest error in long-term prediction compared with other models. The daily closing price datasets of carbon exchanges published in Shenzhen and Beijing are used to validate the efficacy of this methodology. • A modified Gaussian nonlinear trend that can better fit the actual data is proposed. • Incremental Gaussian nonlinear trend fuzzy granulation method with F test is proposed. • A new evaluation index is proposed to make the prediction result more interpretable. [ABSTRACT FROM AUTHOR]

The aggressive industrial upgrade in China's first-tier cities has caused a shift in scholarly attention from peasant migrant workers to skilled migrants. Contrasting with a few elite migrants who are entitled special settlement policies, there is a large group of skilled migrants struggling for permanent homes and settlement amidst the rapid property inflation in the first-tier cities. However, these new floaters' housing experiences and settlement intention have not been studied. This paper fills this gap with a specific focus on skilled migrants' initiatives in expanding access to homeownership from the informal sector and showing how informal homeownership reshapes their settlement intention. Using a structural equation model on a dataset collected from Beijing and Shenzhen, this paper reveals the sorting and enabling effects of informal homeownership (small property rights housing, SPRH) on skilled migrants' settlement intention. The modelling results showed skilled migrants with an intention to settle down in Beijing/Shenzhen tend to buy an informal house as a residence instead of renting a home. Living in owned SPRH encourages migrants' intention to settle down because informal homeownership enables them to offset their structural disadvantages in the formal housing system. Therefore, SPRH serves as a stepping-stone for skilled migrants towards formal homeownership and permanent settlement in China's first-tier cities. Our empirical findings substantiate the socially inclusive nature of SPRH and contributes a new justification to tenure pluralism. • Skilled migrants are struggling for settlement in China's first tier cities. • Their housing experiences and settlement intention have not been studied. • Sorting effects of SPRH: Those with settlement intention tend to buy a SPRH. • Enabling effects of SPRH: Living in owned SPRH encourages settlement intention. • SPRH serves as a stepping-stone for skilled migrants to settle down. [ABSTRACT FROM AUTHOR]

• Knowledge and technologies produced in Chinese cities has increasingly taken a greater share of total output of global cities. • The quality of this output has increased over time Chinese cities improve the quality of their knowledge output before increasing the quality of technology. • Only Beijing, Shanghai & Shenzhen compete globally in terms of quality of output – other Chinese cities do not. • This can be a mechanism for increasing regional inequality within China. Are Chinese cities becoming smarter in terms of the knowledge and technologies they produce? For decades, the widely held believe in the global North is that China merely copies, imitates and only incrementally improves ideas. Recently, this believe is increasingly being challenged. In this paper I quantify this process. Using data from about 6,1 million patents and 60 million academic publications I examine the complexity of technologies and knowledge developed in major Chinese cities and compare these to those produced in other global cities. The results show that (1) knowledge and technologies produced in Chinese cities has increasingly taken a greater share of total output of global cities; (2) the quality of this output has increased over time; (3) Chinese cities improve the quality of their knowledge output before increasing the quality of technology; and (4) only Beijing, Shanghai and Shenzhen compete with other global cities in terms of the quality of output, suggesting emerging regional inequality within China linked to knowledge and technology production. Not only is the size of the Chinese economy increasing, so is the quantity and quality of its knowledge and technology output. [ABSTRACT FROM AUTHOR]