Informal finance in this paper refers to the financing activities of individuals or households to borrow money through channels other than formal financial institutions such as commercial banks. Using data from China Household Finance Survey (CHFS) conducted by Southwestern University of Finance and Economics (SWUFE) and the People's Bank of China, this paper employs Probit model to analyze the factors that may influence the financing needs of Chinese households and factors that influence their likelihood of obtaining loans from formal financial institutions versus from informal channels. Results show that household wealth, family structure, and household head’s characteristics are the major factors that influence their financing needs. Moreover, the results suggest that (a) richer families are more likely to obtain loans from formal financial channels while poorer families are more likely to do so from informal channels; (b) families with stronger social ties are more likely to obtain loans from formal financial channels, but this relationship is weaker in regions where the financial market is more competitive;and (c) the increase of formal financial services is positively related to the probability of households obtaining formal finance, but has no relationship with the probability of households obtaining informal finance. These findings have important implications for finance policy making.

With the rapid rise of distributed generation, Internet of Things, and mobile Internet, both U.S. and European smart home manufacturers have developed energy management solutions for individual usage. These applications help people manage their energy consumption more efficiently. Domestic manufacturers have also launched similar products.

This paper focuses on the factors influencing Energy Management Behaviour (EMB) at the individual level. By reviewing academic literature, conducting surveys in Beijing, Shanghai and Guangzhou, the author builds an integrated behavioural energy management model of the Chinese energy consumers. This paper takes the vague term of EMB and redefines it as a function of two separate behavioural concepts: Energy Management Intention (EMI), and the traditional Energy Saving Intention (ESI).

Secondly, the author conducts statistical analyses on these two behavioural concepts. EMI is the main driver behind an individual’s EMB. EMI is affected by Behavioural Attitudes, Subjective Norms, and Perceived Behavioural Control (PBC). Among these three key factors, PBC exerts the strongest influence. This implies that the promotion of the energy management concept is mainly driven by good application user experience (UX). The traditional ESI also demonstrates positive influence on EMB, but its impact is weaker than the impacts arising under EMI’s three factors. In other words, the government and manufacturers may not be able to change an individual's energy management behaviour if they rely solely on their traditional promotion strategies. In addition, the study finds that the government may achieve better promotional results by launching subsidies to the manufacturers of these kinds of applications and smart appliances.