China is confronting great pressure to reduce carbon emissions. This study focuses on the driving factors of carbon emissions in China using the Logarithmic Mean Divisia Index (LMDI) method. Seven economic factors, including gross domestic product (GDP), investment intensity, research and development (R&D) intensity, energy intensity, research and development (R&D) efficiency, energy structure and province structure are selected and the decomposition model of influencing factors of carbon emissions in China is constructed from a sectoral perspective. The influence of various economic factors on carbon emissions is analyzed quantitatively. Results show that the R&D intensity and energy intensity are the main factors inhibiting the growth of carbon emissions. GDP and investment intensity are the major factors promoting the growth of carbon emissions. The contribution of R&D efficiency to carbon emissions is decreasing. The impacts of energy structure and province structure on carbon emissions are ambiguous through time. Finally, some policy suggestions for strengthening the management of carbon emissions and carbon emission reduction are proposed.
Forecasting energy demand in emerging nations is a critical policy tool utilized by decision makers worldwide. However, as estimated economic and demographic characteristics frequently diverge from realizations, precise forecast results are difficult to get due to the economic system’s intrinsic complexity. This work proposed a machine learning model for estimating energy consumption in China using the support vector regression model (SVR). Additionally, Markov Chain (MC) is employed to forecast and analyze the evolving energy consumption structure. The results demonstrate that SVR model is more accurate (98.4%) than the linear model (Moving Average model), the nonlinear model (Grey model), and past research in predicting energy usage. Under the current rate of energy consumption, China’s total energy consumption will break through six billion in the next 4 years. Furthermore, it is expected that China’s energy consumption structure will be more rational in 2025, with increased non-fossil energy consumption and decreased coal consumption, while natural gas consumption continues to grow at a low rate. It provides scientific basis for the implementation of carbon emission peak action, energy security and energy development plan during the 14th Five-Year Plan period.
PurposeThe purpose of this paper is to examine the effectiveness of an improved dummy variables control grey model (DVCGM) considering the hysteresis effect of government policies in China's energy intensity (EI) forecasting.Design/methodology/approachEnergy consumption is considered as an important driver of economic development. China has introduced policies those aim at the optimization of energy structure and EI. In this study, EI is forecasted by an improved DVCGM, considering the hysteresis effect of energy-saving policies of the government. A nonlinear optimization method based on particle swarm optimization (PSO) algorithm is constructed to calculate the hysteresis parameter. A one-step rolling mechanism is applied to provide input data of the prediction model. Grey model (GM) (1, N), DVCGM (1, N) and ARIMA model are applied to test the accuracy of the improved DVCGM (1, N) model prediction.FindingsThe results show that the improved DVCGM provides reliable results and works well in simulation and predictions using multivariable data in small sample size and time-lag virtual variable. Accordingly, the improved DVCGM notes the hysteresis effect of government policies and significantly improves the prediction accuracy of China's EI than the other three models.Originality/valueThis study estimates the EI considering the hysteresis effect of energy-saving policies in China by using an improved DVCGM. The main contribution of this paper is to propose a model to estimate EI, considering the hysteresis effect of energy-saving policies and improve forecasting accuracy.
This study focuses on the investment e ciency of renewable energy enterprises and how they respond to government green scal policies. It complements previous research that only examines corporate investment e ciency from government subsidies or tax incentives. Moreover, we extend the impact of green scal policies on the investment ine ciency of renewable energy enterprises from the perspective of property heterogeneity. We choose nancial data of 158 renewable energy enterprises for both state-owned (SOEs) and private (Non-SOEs) listed on the Shanghai and Shenzhen Stock Exchange from 2010-2018 and use the Richardson model to measure the investment e ciency. The xed-effect panel model is applied to explore the impact of green scal policies on the investment e ciency of renewable energy enterprises. The results show that, in general, green nancial subsidies aggravate over-investment, especially for SOEs, while green tax incentives alleviate under-investment considerably, especially for Non-SOEs. We also nd ine cient investment pervasive in China, among which the over-investment problem more serious than underinvestment. But for the Non-SOEs, under-investment is more predominant. Finally, we propose that government subsidies for SOEs should be constrained and supervised, while more exible tax incentives for Non-SOEs should be advocated.
This study focuses on the investment efficiency of renewable energy enterprises and how they respond to government green fiscal policies. It complements previous research that only examines corporate investment efficiency from government subsidies or tax incentives. Moreover, we extend the impact of green fiscal policies on the investment inefficiency of renewable energy enterprises from the perspective of property heterogeneity. We choose financial data of 158 renewable energy enterprises for both state-owned (SOEs) and private (Non-SOEs) listed on the Shanghai and Shenzhen Stock Exchange from 2010-2018 and use the Richardson model to measure the investment efficiency. The fixed-effect panel model is applied to explore the impact of green fiscal policies on the investment efficiency of renewable energy enterprises. The results show that, in general, green financial subsidies aggravate over-investment, especially for SOEs, while green tax incentives alleviate under-investment considerably, especially for Non-SOEs. We also find inefficient investment pervasive in China, among which the over-investment problem more serious than under-investment. But for the Non-SOEs, under-investment is more predominant. Finally, we propose that government subsidies for SOEs should be constrained and supervised, while more flexible tax incentives for Non-SOEs should be advocated.
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