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Tianhao Zhi

Publications and source records attributed to Tianhao Zhi.

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The dynamics of innovation diffusion: A survey of Bass-type models

This paper synthesises the existing research on the dynamics of innovation diffusion, with a focus on Bass-type models and their extensions. The theoretical foundation of innovation diffusion proposed by Rogers (1962) and the seminal work of Bass (1969) serve as a starting point for the analysis. We identify and examine various generalizations and stochastic extensions of the Bass model, including counting processes, diffusion processes, and uncertain processes, as well as parameter estimation techniques, from classical statistical techniques to more advanced techniques such as Bayesian filtering and metaheuristic optimisation. We finally explore alternative models of innovation diffusion, with a particular focus on agent-based models. This overview of the evolution of Bass-type models illustrates the progress made in innovation diffusion research over the past decades.

econ.GN

Sustainability Risks under Lotka-Volterra Dynamics

The record-breaking heat in recent years, along with other extreme weather conditions worldwide has not only warned us about the devastating effects of global warming but also revived our interest in studying sustainability risks on a broader scale. In this paper, we propose a generalised model of sustainability risks characterising the economic-environmental-social nexus (EVS) based on a classic Lotka-Volterra framework. Compared to the World3 model proposed by Meadows et al. (1972) in their landmark study "The Limits to Growth", our model has numerous advantages such as i) better analytical tractability, ii) more representative characterisation of economic development arising from innovation, and iii) can be adopted in many potential applications of modelling sustainability risks from its sub-dynamics.

econ.GN

The Future of Traditional Fuel Vehicles (TFV) and New Energy Vehicles (NEV): Creative Destruction or Co-existence?

There is a rapid development and commercialization of new Energy Vehicles (NEV) in recent years. Although traditional fuel vehicles (TFV) still occupy a majority share of the market, it is generally believed that NEV is more efficient, more environmental friendly, and has a greater potential of a Schumpeterian "creative destruction" that may lead to a paradigm shift in auto production and consumption. However, less is discussed regarding the potential environmental impact of NEV production and future uncertainty in R&D bottleneck of NEV technology and innovation. This paper aims to propose a modelling framework based on Lux (1995) that investigates the long-term dynamics of TFV and NEV, along with their associated environmental externality. We argue that environmental and technological policies will play a critical role in determining its future development. It is of vital importance to constantly monitor the potential environmental impact of both sectors and support the R&D of critical NEV technology, as well as curbing its negative externality in a preemptive manner.

econ.GN

Is there a housing bubble in China

There is a growing concern in recent years over the potential formation of bubbles in the Chinese real estate market. This paper aims to conduct a series of bubble diagnostic analysis over nine representative Chinese cities from two aspects. First, we investigate whether the prices had been significantly deviating from economic fundamentals by applying a standard Engle-Granger cointegration test. Second, we apply the Log-Periodic-Power-Law-Singularity (LPPLS) model to detect whether there is any evidence of unsustainable, self-reinforcing speculative behaviours amongst the price series. We propose that, given the heterogeneity that exists amongst cities with different types of bubble signatures, it is vital to conduct bubble diagnostic tests and implement relevant policies toward specific bubble characteristics, rather than enforcing one-that-fits-for-all type policy that does not take into account such heterogeneity.

q-fin.RM