• Energy Research

China’s macroeconomic policy framework has been determined to ensure steady growth, adjust the industrial structure and advance the socioeconomic reforms in recent years. And urbanization is supposed to be one of the most important socioeconomic reform directions. Meanwhile, China also committed to reduce carbon emissions intensity by 2020, then it should be noted that what kind of impact of these policy orientations on carbon emission intensity. Therefore, based on the historical data from 1978 to 2011, this paper quantitatively studies the impact of China’s economic growth, industrial structure and urbanization on carbon emission intensity. The results indicate that, first, there is long-term cointegrating relationship between carbon emission intensity and other factors. And the increase in the share of tertiary industry [i.e., the ratio of tertiary industry value added to gross domestic product (GDP)] and economic growth (here we use the real GDP per capita) play significant roles in curbing carbon emission intensity, while the promotion of population urbanization (i.e., the share of population living in the urban regions of total population) may lead to carbon emission intensity growth. Second, there exists significant one-way causality running from the urbanization rate and economic growth to carbon emission intensity, respectively. Third, among the three drivers, economic growth proves the main influencing factor of carbon emission intensity changes during the sample period.

Abstract Given the close contact between international financial markets, the contagion effect across markets is becoming increasingly obvious. In this paper, which uses principal component analysis to build a Chinese stock market investor sentiment index and further applies a structural vector autoregression (SVAR) model, we analyze the contagion effect of international crude oil price fluctuations on Chinese stock market investor sentiment. The results show that international crude oil price fluctuations significantly Granger cause Chinese stock market investor sentiment; in the long term, if the international crude oil price fluctuates by 1%, stock market sentiment will negatively fluctuate 3.9400%. From the perspective of short-term efficacy, if the international crude oil price fluctuates by 1%, stock market investor sentiment in the same period will negatively fluctuate 1.0223%. International crude oil prices made a greater early contribution to investor sentiment and showed a rapid growth trend, with a contribution of 2.8076% in the first period and 8.1955% in the second. The growth rate then slows and eventually stabilizes at the 25% level; the average contagion delay for international crude oil price fluctuation to affect investor sentiment is 8 months.

AbstractThis paper develops a time‐varying parameter vector autoregressive model to examine the dynamic effects of crude oil prices and monetary policy on China's economy during January 1996 to June 2017. The empirical results indicate that (a) in general, international crude oil price shocks have positive effect on China's economic growth and inflation in the short run, but the long‐run effect appears diverse; (b) China's monetary policy shocks have positive effect on the economic growth and inflation overall; specifically, an increase in monetary supply can partly offset crude oil prices' negative effect on China's economic growth; (c) China's monetary policy has positive effect on crude oil prices and plays an important role in the relationship between crude oil price shocks and economy; and (d) during the recent global financial crisis, crude oil price shocks produce greater negative effect on China's economic growth, whereas the long‐run effect of monetary policy on China's economic growth proves weaker, compared with other periods.

The quantity of China's private cars has increased dramatically in the past decade, which has become one of the key sources of carbon emission and air pollution in the cities of China. In theory, to improve energy efficiency can reduce carbon emission significantly, but the result may be affected by the rebound effect. This paper utilizes a two-stage Almost Ideal Demand System (AIDS) model to estimate the total CO2 rebound effect for China's private cars during 2001–2012 at the provincial level, then uses a panel data model to analyze its impact factors. The results suggest that, first of all, the CO2 emissions of private cars have the super conservation effect, partial rebound effect and backfire effect among provinces in China. And the direct CO2 rebound effect plays a dominant role in the total CO2 rebound effect in most provinces. Second, the total CO2 rebound effect of private cars among China's provinces presents an overall convergence trend over time. Finally, the household expenditure and the population density have a negative and positive influence on the total CO2 rebound effect for China's private cars, respectively.

According to the combined principles of fairness and efficiency, a comprehensive allocating indicator system is developed, and the TOPSIS approach is applied to allocate China’s 40–45 % carbon emission intensity (carbon emission per unit of GDP) reduction target by 2020. The results indicate that, first of all, the unequally weighted indicator system outperforms the equally weighted one according to regional developing situation in China; and the most important indicator affecting the allowance allocation is carbon reduction responsibility, followed by future development right and emission reduction efficiency. Second, China’s carbon emission intensity should be cut, but its absolute carbon emission volume may inevitably increase in the future due to the continuous economic growth, and we confirm that the western provinces may take the highest shares to increase carbon emissions, followed by the central, northeast and eastern provinces. Finally, in order to achieve the national target of carbon emission intensity reduction, the northeast and eastern provinces require reducing carbon emission intensity significantly from 2013 to 2020, while the central and western provinces should be given more developing room.

Abstract Given that the gold market and the crude oil market are the main representatives of the large commodity markets, it is of crucial practical significance to analyze their cointegration relationship and causality, and investigate their respective contribution, from the perspective of price discovery, to the common price trend so as to interpret the dynamics of the whole large commodity market and forecast the fluctuation of crude oil and gold prices. Empirical analysis indicates that, first, there are consistent trends between the crude oil price and the gold price with significant positive correlation coefficient 0.9295 during the sampling period, from January of 2000 to March of 2008. Second, there can be seen a long-term equilibrium between the two markets, and the crude oil price change linearly Granger causes the volatility of gold price, but not vice versa; moreover, the two market prices do not face a significant nonlinear Granger causality, which overall suggests their fairly direct interactive mechanism. Finally, with regard to the common effective price between the two markets, the contribution of the crude oil price seems larger than that of the gold price, whether with the permanent transitory (PT) model (86.50% versus 13.50%) or the information share (IS) model (50.28% versus 49.72%), which implies that the influence of crude oil on global economic development proves more far-reaching and extensive, and its role in the large commodity markets has attracted more attention in recent years.

Currently European Union Allowances (EUA) and secondary Certified Emission Reduction (sCER) have become two dominant carbon trading assets for investors and their linkage attracts much attention from academia and practitioners in recent years. Under this circumstance, we use the empirical mode decomposition (EMD) approach to decompose the two carbon futures contract prices and discuss their correlation from the multi-frequency perspective. The empirical results indicate that, first, the EUA and sCER futures price movements can be divided into those triggered by the long-term, medium-term and short-term market impacts. Second, the price movements in the EUA and sCER futures markets are primarily caused by the long-term impact, while the short-term impact can only explain a small fraction. Finally, the long-term (short-term) effect on EUA prices is statistically uncorrelated with the short-term (long-term) effect of sCER prices, and there is a medium or strong lead-and-lag correlation between the EUA and sCER price components with the same time scales. These results may provide some important insights of price forecast and arbitraging activities for carbon futures market investors, analysts and regulators.

PurposeRisk aversion is considered as an important factor in predicting asset prices. Many studies have proved that there exists important price information spillover among crude oil, precious metals and agricultural markets. Then there naturally follows the question: Is the risk aversion of investors in crude oil market predictable for the returns of precious metals and agricultural products? The purpose of this paper is to answer this question. For this reason, the authors explore the directional predictability and the cross-quantile dependence between risk aversion of crude oil market investors and returns of precious metals and agricultural products.Design/methodology/approachTo better describe the risk aversion of investors, this paper uses high-frequency data and model-free calculation method to obtain variance risk premium of crude oil. Then, this paper uses the cross-quantilogram method to investigate the directional predictability and cross-quantile dependence between risk aversion of crude oil market investors and returns of precious metals and agricultural products. Meanwhile, it employs the partial cross-quantilogram (PCQ) method to test the impact of control variables on the empirical results.FindingsFirstly, risk aversion of crude oil market investors has directional predictability for returns of precious metals and agricultural products. Secondly, different degrees of risk aversion of crude oil market investors have different impacts on returns of precious metals and agricultural products. A low (high) degree of crude oil market investors' risk aversion has negative (positive) predictability for returns of precious metals and agricultural products. Finally, during the sample period, the returns of precious metals are more affected by risk aversion of crude oil market investors than returns of agricultural products.Originality/valueFirst of all, this paper studies the impact of risk aversion of crude oil market investors on returns of precious metals and agricultural products. It updates previous relevant studies on the factors influencing the prices of precious metals and agricultural products, and provides a new idea for the forecast of those commodity returns. Secondly, this paper provides the evidence that different degrees of risk aversion of investors have different effects on the returns of commodities, and expands the research on the topic of commodity returns prediction. Finally, high-frequency data are employed in this paper to better capture the risk aversion of investors than commonly used daily data.

Abstract Existing calculated benchmarking methods and main energy performance assessment schemes often lack the practical ability to manage the energy performance of a vast number of widespread telecommunication base stations (TBSs). Therefore, on the basis of a TBS survey, this paper puts forward the dynamic simulation and sensitivity analysis method to allow the new rule “one energy benchmark for a group of similar TBSs” rather than the traditional rule “one benchmark for one assessed building”. The new method reasonably limits the number of benchmarks and a feasible benchmark system is established for managing numerous TBSs. The results indicate that, first, more than one million TBSs distributed in a large area of China can be divided into 448 typical scenarios. Second, the benchmarks for reasonable energy use of these scenarios can be organized into four simple benchmarking charts. Third, the attempt to establish further challenging energy benchmarks shows that the most energy-saving measure in TBSs, i.e., ventilation cooling, can fully eliminate the negative impact of poor configurations of envelops and cooling coefficients of performance (COP). Finally, establishing telecom industrial standards for locating the reasonable TBS energy consumption level even in giant countries appears feasible.