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This study constructs a comprehensive energy price index and applies the Morlet continuous wavelet transform to investigate the relationship between energy prices and energy industry stock markets. It also tests the multi-scale linear and nonlinear causality using multi-resolution decomposition. The empirical findings indicate that: (1) energy prices and stock market volatilities demonstrate a stable negative correlation in the medium- and long-term frequency domain, and the fluctuation of energy stock prices precedes that of energy prices since 2018. (2) Energy prices and stock markets exhibit bidirectional causality. The short- and medium-term driving effects of energy stocks and energy prices are more pronounced than the overall change in stock markets over a period of 16 months. The linkage between energy prices and stock markets is primarily influenced by the stock market leading and driving energy price changes, indicating a common long-term trend. (3) In the long run, fluctuations in Chinese stock markets will lead to a reverse change in energy prices, providing policy management in the energy industry with an effective reference. However, the unstable short-term characteristics and lag of energy price changes suggest that the impact of energy prices on stock market investment has less reference value.

This study pioneers investigating the environmental Kuznets curve (EKC) hypothesis and its possible regional differential characteristics in China. Based on 2003–2017 years of provincial panel data, this research employs panel smooth transition auto regression (PSTR) model to analyze the impact of income levels on environmental pollution and identify the EKC threshold of energy intensity. Then, 30 Chinese provinces were categorized by energy intensity to examine the inter-provincial and interregional differences in the EKC threshold of energy intensity, rather than the traditional threshold of income. The results indicate that China's pollution emissions and energy intensities show a stepwise decreasing pattern from the western region to the eastern region. In addition, the impact of income levels on pollution emissions is non-linear, and the critical value of energy intensity is 0.9168 between the high- and low regimes. An inverted U-shaped environmental Kuznets curve is accepted for energy intensity, with 0.7670 as its threshold value. When the energy intensity is higher (lower) than the threshold value, the income elasticity of pollution emission is positive (negative). The more developed provinces and municipalities mainly in the eastern region are proved to exceed the threshold than the provinces in the central and western regions within the sample period. Given these findings, this study further divides China's provinces into eco-friendly, low-pollution, and high-pollution provinces, and accordingly, the most important policy recommendations were discussed for policy-makers and researchers. This research can provide a new insight to investigate countries with unbalanced development levels within and a reference for environmental governance policies in sub-regions.

Introducing heterogeneous R&D subsidies into a two-sector technological model, this study investigates the effect of subsidies and directed technologies on environmental quality, and estimates the dynamic process of technological change direction and environmental quality with heterogeneous subsidies on China's auto industry. The results show that both clean and non-clean technologies have a strong effect on environmental quality: non-clean technology could improve environmental quality by promoting energy efficiency and environment benefits from clean technology by increasing new energy vehicles. However, it is difficult for clean technology to substitute non-clean technology as dominated technological application. R&D subsidies can change technological direction to affect environmental quality. In addition, non-clean R&D subsidies are found to improve environmental quality in a better way in the initial 15 years during the simulation period, but clean R&D subsidies prove to be more effective to improve environmental quality in the long run.

Using the monthly Producer Price Index (PPI) of 37 different industries in China from 2007 to 2017, this study established a Markov regime -switching model to analyze the regime differences and industry heterogeneity of the transmission of volatility from energy price to PPI. The results showed that volatility in energy prices within a given regime has a relatively high “inertia”. Regardless of being in a high- or low-volatility regime, volatility in energy prices was found to have a positive transmission effect on the PPI. In addition, the transmission coefficients of the low-volatility regime tended to be higher than the transmission coefficients of the high-volatility regime. Further analysis revealed that the impact of the volatility in energy prices on the PPI of factors of production was significantly greater than the impact on the PPI of consumer products. Furthermore, in terms of industry heterogeneity, the transmission coefficients of energy and resource industries were found to be the greatest, followed by energy- and resource-intensive industries, and labor-intensive industries; the transmission coefficients of technology- and capital-intensive industries were the lowest.

Using the trans-log cost function and elasticity model, this paper pioneered the analysis of the elasticity and characteristics of the substitution between energy consumption and capital in China's manufacturing industry between 1995 and 2014. Firstly, the research revealed significant differences in the magnitude and direction of the substitution between coal, electricity, petroleum, and capital, thus demonstrating heterogeneity in the substitution. Secondly, the analysis, based on the Morishima Elasticity of Substitution (MES) model showed that: 1) depending on what triggers changes in factor prices, the manufacturing sector shows a reverse asymmetry whereby capital substituted energy and energy complemented capital 2) a significant asymmetry exists in the direction and magnitude of substitution in capital- and labor-intensive sectors. This asymmetric effect indicates that China can attain overall energy conservation and apply energy-saving technologies in the manufacturing industry by implementing the substitution of capital for energy. However, the differences with regards to the forms of energy consumption among sectors, as well as the heterogeneity and asymmetry in capital–energy substitution should be considered in research on energy policies for the manufacturing industry. This should also involve the integration of energy sources to ensure that such policies are targeted and effective.

Under the influence of major random events such as US-China trade dispute and the coronavirus (COVID-19) outbreak, the uncertainty of macroeconomic regulation has also risen significantly. Then, what role economic policy level shocks and uncertainty shocks play in industrial regulation need to be clarified urgently. This study constructs a large-scale benchmark VAR model including fiscal and monetary policy and three-industry output and inflation, makes a detailed distinction between policy level shocks and uncertainty shocks, and describes the industrial control mechanism of economic policy from a new perspective. The main conclusions are as follows: firstly, compared with the ordinary VAR model, the benchmark VAR model has significant advantages in modeling efficiency, system stability and explanatory power, making it more suitable for describing the transmission mechanism of economic policy. Secondly, in terms of the role of policy uncertainty, its influence sometimes even exceeds the level impact, but in most cases, it is manifested as suppressing the output of the three industries, and the impact on industrial inflation is also dual at the same time. It shows that policy uncertainty is prone to adverse effects, and policy authorities should be more cautious in its use. Finally, from perspective of the adjustment of the three industries, tax and fee reduction are a relatively dominant means to optimize the output of the three industries, while in terms of maintaining price stability, the steady interest rate regulation is more efficient.

This study first used principal component analysis to calculate an operating index for China's energy industry and applied the index to investigate cyclical fluctuation trends. Subsequently, a time-varying parameter structural vector auto-regressive model was adopted to analyse the time-varying characteristics of the cyclical fluctuations in the macro economy and carbon emissions. The results showed that cyclical changes in the energy industry lagged fluctuations in the macroeconomic environment. The average length of the cycle was three years. Industrial shocks had a clear pro-cyclical impact on production levels and prices. During periods of prosperous growth, these positive shocks can sustain increased output and price levels. During recessionary periods, positive shocks have a short-term effect and are not favourable for output growth or price rebounds in the long run. The macroeconomic effect of cyclical fluctuations in the energy industry clearly has a stronger price effect than output effect and a greater impact on producer price inflation than consumer price inflation. In addition, the effect of energy shocks on carbon emissions has gradually weakened over time. We argue that promoting supply-side structural reform policies and eliminating excessive energy capacity within the industry while continuing to promote green energy strategies are key solutions to reversing supply-demand imbalances and downward price trends.

Forecasting total energy demand and its structure is the basis for energy planning and industrial policy formulation. However, existing research on the forecast of energy structure remains inadequate. This study aims at constructing an ADL–MIDAS model to identify the optimal model to forecast China's energy demand and its structure, and offer a reasonable judgement on future carbon emission and energy scenarios in China and other developing countries. Thus, this study adopts mixed frequency data for quarterly GDP, quarterly added value, and annual energy demand of various industries to construct an ADL–MIDAS model. Then, the optimal model to forecast China's energy demand is selected from various model combinations that employ different weight functions and forecasting methods. The model forecasts China's total energy demand and its structure as proposed in the 13th Five Year Plan. The in-sample prediction results show that, in the optimal model, the smallest prediction error is 0.02%, while the largest of the four future periods is 2%, indicating that the ADL–MIDAS model is effective in forecasting energy demand. Further, the forecast results suggest that, by 2020, China's total energy demand will reach approximately 4.65 billion tonnes of standard coal equivalent; the demand for coal, natural gas, and non-fossil fuel will be 57%, 7.6%, and 18%, respectively, contingent on economic growth conditions. Given these forecast results, the energy planning targets set under the 13th Five Year Plan are attainable. However, in the case of natural gas demand, considerable marketing is required to promote its use.

This paper depicts the natural gas price control behavior of the Chinese government in an imperfect competition market structure using a static CGE model. It simulates the effect of changes in natural gas price control policy on carbon emission, and the economic effects in view of demand side and supply side. Based on the above, we also analyze the carbon emission mechanism from economic and energy structure perspective. The results show that: an increase in natural gas price can reduce carbon emission, or tends to cause a long-term decline in the surplus profit rate of the natural gas industry. Moreover, the increase in natural gas price may raise the CPI, and reduce actual GDP and residents’ welfare. On the contrary, a decrease in natural gas price may reduce CPI and enhance resident welfare. However, the long-term actual GDP will not increase, but carbon emission will increase and the surplus profit rate of the natural gas industry may reduce in the short term and increase in the long term. On the other hand, Both increase and decrease in natural gas prices may result in a decrease of the actual GDP level in the long term. The elimination of price control in natural gas supply may increase actual GDP and residents’ welfare and reduce CPI. Meanwhile, it may increase carbon emissions and improve the profitability of the natural gas industry.

Based on the modified DMSP night-time light composite data, this paper constructs an urban polycentric index, then uses a time-varying panel model to estimate the empirical equations of smog pollution. The empirical results show that: (1) with the long-term steady improvement of China’s per capita income, the income growth and air quality have shown a clear coordinated development trend (2) the regional multi-centered development has a significant mechanism of promoting the rise and then the fall of smog pollution (3) China’s secondary industry and transportation industry no longer have significant pollution-promoting effect at present, and are upgrading to environment-friendly industries. Besides, this study makes some additional contributions: On the one hand, the correction of night-time light data can provide more accurate data processing criterion for related research. On the other, the time-varying panel estimation further explains the complex mechanism between economic variables and smog pollution as well as reveals the origin of the instability of the EKC hypothesis. These improvements may provide an important academic reference for the subsequent researches and a general empirical evidence for policy makers.

Environmental regulation and green technology progress are important means to solve the negative externalities of the environmental pollution. Despite the widespread concern on the impact of environmental regulation on green technology progress, the existing literatures focus on the relationship between them mainly in the local parts of the same regions, lack of further convincing investigation from local-neighborhood perspective. Based on the spatial panel Durbin model, this paper examines the ripple effect of environmental regulation in different adjacencies by using the data of the provincial level in China, and further investigates the formation mechanism of ripple effect in local-neighborhood environmental regulation combined with the characteristic facts in regional industrial investment structure transformation. The results show that environmental regulation has a ripple effect, namely, environmental regulation can not only affect the green technology progress in the local regions but also change the innovation of green technology in the neighboring regions. Both of them show a U-shaped change characteristic. When subdividing industrial neighborhood from the economic neighborhood, the ripple effect of environmental regulation in geographical neighboring or industrial neighboring regions is significant, but this ripple effect in the pure economic neighboring regions is not significant. The ripple effect of geographic neighboring environmental regulation in neighboring overlapping areas is significantly strengthened. Given two regions with close industries distance, the closer the geographical distance is, the more ripple effect of environmental regulation will present. Environmental regulation will restrain the green technology innovation of neighboring regions by increasing investment in polluting industries in the neighboring regions, but the income growth effect of industrial transfer will not enhance the green innovation capacity in the neighboring regions and will deteriorate permanently.

This article analyzed the principle of theory and dynamic mechanism on distribution and manufacturing sector co-evolution. Empirical study was based on collaborative degree model and co-evolution mode, applying the data of year 1986-2010 distribution and manufacturing sector of China. The empirical study demonstrated that, there existed interaction relations between distribution and manufacturing sector. The combined system of distribution and manufacturing sector was cooperative system. An equilibrium stable solution was existed in system evolution. Study also found out, the synergy degree of combined system for distribution and manufacturing sector was relatively low, but in collaborative development status overall. Order degree of manufacturing sector was higher than distribution sector, but both parties showed significant consistency.