Methane accounts for 20% of the global warming caused by greenhouse gases, and wastewater is a major anthropogenic source of methane. Based on the Intergovernmental Panel on Climate Change greenhouse gas inventory guidelines and current research findings, we calculated the amount of methane emissions from 2000 to 2014 that originated from wastewater from different provinces in China. Methane emissions from wastewater increased from 1349.01 to 3430.03 Gg from 2000 to 2014, and the mean annual increase was 167.69 Gg. The methane emissions from industrial wastewater treated by wastewater treatment plants (EIt) accounted for the highest proportion of emissions. We also estimated the future trend of industrial wastewater methane emissions using the artificial neural network model. A comparison of the emissions for the years 2020, 2010, and 2000 showed an increasing trend in methane emissions in China and a spatial transition of industrial wastewater emissions from eastern and southern regions to central and southwestern regions and from coastal regions to inland regions. These changes were caused by changes in economics, demographics, and relevant policies.

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Adaptive integral sliding mode control (AISMC) is an extension of adaptive sliding mode control which is a way to ensure sliding motion while handling system uncertainties. However, conventional AISMC formulations require to different extent a priori knowledge of the system uncertainty: either the upper bound of the uncertainty or of its time derivative are assumed to be bounded a priori, or the uncertainty is assumed to be parametrized by some structure-dependent factorization. This work proposes a variant of AISMC with reduced a priori knowledge of the system uncertainty: it is shown that Euler-Lagrange dynamics typical of sliding mode literature admit a structure-independent parametrization of the system uncertainty. This parametrization is not the result of structural knowledge, but it comes from basic properties of Euler-Lagrange dynamics, valid independently on the structure of the system. The AISMC control method arising from this parametrization is analyzed in the Lyapunov stability framework, and validated in systems with different structures: a surface vessel and an aerial vehicle.

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Purpose – The paper is aimed to obtain a clear understanding of influence factors that can increase the possibility to be business angels (BA). Design/methodology/approach – This study develops the 3A model in the Chinese context to design questionnaire, and 334 questionnaires are obtained via focus group sample and targeted snowball approach, and the multinomial logit analysis is used to test a serious of hypotheses. Findings – The paper confirmed that the entrepreneurial experience and wealth are determinants of investment for potential BA, and the wealth have both directly and indirectly positive influence on investment activity through risk preference, namely that richer people prefer risk which impel them to invest as BA. Research limitations/implications – There are two limitations in the paper: first, the macro environment in China has not been taken into consideration in the model; second, the source of the sample focuses on the developed cities in the middle and eastern of China, only reflect the characteristic of angels in these areas, which may somewhat diverges from the reality. Practical implications – The paper would contribute to form the policy which could promote the development of angel investment in China. Originality/value – This paper conducts a preliminary exploration of the factors that have impact on Chinese BA' investment activity based on current research.