The space-temporal evolution of economic inequality is examined with Markov chain test method, and the dynamic interrelationships among environmental quality, energy consumption, and economic inequality in China from the province-level are tested by focusing on accounting for structural shifts in causal linkages in this paper. We first employ the Toda-Yamamoto causality framework and then augment it with a Fourier approximation which captures structural changes as a smooth process. The empirical findings show that taking into account smooth structural shifts is important for the causal linkages between economic inequality and energy consumption, and also between environmental quality and energy consumption. The causality analysis with structural changes provides a causal linkage between economic inequality and energy consumption in 26 out 30 provinces and a causal linkage between environmental quality and energy consumption in 7 out 30 provinces, while the quantities are 22 out 30 and 5 out 30 respectively when not accounting for structural shifts. These findings are consistent with the fact that provincial economics in China have experienced structural changes in economy-environment-energy sectors. We also conduct additional analyses which point out that regional and cyclical dependency matter for the causal relationships, and the method of HP filtering can not effectively solve the problem of smooth shifts in economy-environment-energy causality.
This paper proposes a new time-varying parameter distributed lag (DL) model. In contrast to the existing methods, which assume parameters to be random walks or regime shifts, our method allows time-varying coefficients of lagged explanatory variables to be conditional on past information. Furthermore, a test for constant-parameter DL model is introduced. The model is then applied to examine time-varying causal effect of inventory on crude oil price and forecast weekly crude oil price. Time-varying causal effect of US commercial crude oil inventory on crude oil price return is presented. In particular, the causal effect of inventory is occasionally positive, which is contrary to some previous research. It's also shown that the proposed model yields the best in and out-of-sample performances compared to seven alternative models including RW, ARMA, VAR, DL, autoregressive-distributed lag (ADL), time-varying parameter ADL (TVP-ADL) and DCB (dynamic conditional beta) models.
Cities generate more than 60% of carbon emissions and are the main battleground for achieving the target. However, there is no unified and standardized measurement methods of carbon emissions in cities. In this paper, we took Xi'an as an example and started by measuring carbon emissions with the new standards. Then, the decoupling of economic development from carbon emissions was studied according to the Tapio decoupling theory. Based on the generalized Divisia index method, the decoupling effort model was proposed to study the impact of carbon emission factors contributing to carbon reduction. The results show: (ⅰ) During the period 1995–2021, the carbon emissions of Xi'an increased rapidly, with an average annual growth rate of 6.06%, due to the accelerating pace of urbanization and industrialization. (ⅱ) The energy consumption sector accounted for the largest share of carbon emissions, ranging from 77.38% to 89.46%. Xi'an's energy structure is primarily based on fossil fuels, especially coal, which holds a significant proportion. To achieve the "double carbon" goal, it is crucial to reduce the dependence on fossil fuels. (ⅲ) The 10th Five-Year Plan was in the state of "expansive coupling", while other periods were in the "weak decoupling" state from the 9th to 14th Five-Year Plan periods. After the carbon peak year in the 15th Five-Year Plan, it would be in a state of "strong decoupling". The agricultural production account was the first to achieve a "strong decoupling" state. (ⅳ) The government of Xi'an made efforts to decouple, but these were not enough. Technological innovation played a crucial role in the carbon reduction of Xi'an, and was a key factor in achieving the "double carbon" goal.
The bond market is an important market for investment and financing in China's economic sectors, and also an important part of the monetary policy framework. The internal transmission of bond market is an important part of market interest rate transmission, which iscritical to the effectiveness of monetary policy. However, few scholars have studied the characteristics of interest rate transmission in China. An in-depth study of the interest rate transmission mechanism and its dynamic evolution between different bond markets is conducive to clarify the pulse of transmission within Chinese bond market and to further unblock the transmission mechanism of monetary policy. From the perspective of system theory and based on the analysis method of Granger causality complex network, this paper finds that the interest rate transmission among various varieties in China's bond market is relatively significant. Treasury bonds and CDB bonds are the two core bond varieties of interest rate transmission in the bond market. Simultaneously, this study concludes that the medium and long-term interest rate played a dominant role in the transmission of market interest rate during the easing phase of monetary policy, while the short-term interest rate played a dominant role in the transmission of market interest rate during the tightening phase of monetary policy. This paper also gives enlightenment and suggestions.
In this paper, stochastic global optimization algorithms, specifically, genetic algorithm and simulated annealing are used for the problem of calibrating the dynamic option pricing model under stochastic volatility to market prices by adopting a hybrid programming approach. The performance of this dynamic option pricing model under the obtained optimal parameters is also discussed. To enhance the model throughput and reduce latency, a heterogeneous hybrid programming approach on GPU was adopted which emphasized a data-parallel implementation of the dynamic option pricing model on a GPU-based system. Kernel offloading to the GPU of the compute-intensive segments of the pricing algorithms was done in OpenCL. The GPU approach was found to significantly reduce latency by an optimum of 541 times faster than a parallel implementation approach on the CPU, reducing the computation time from 46.24 minutes to 5.12 seconds.