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Due to the implementation of “electrical energy substitution” strategy in China, the proportion of electrical energy in terminal energy consumption is increasing. The improvement of electrical energy efficiency could increase overall energy efficiency. Thus, a special attention should be paid on electrical energy efficiency. An input-oriented epsilon-based measure-DEA (data envelopment analysis) model was used to measure electrical energy efficiency from the perspective of total factor, and the spatial-temporal variability of electrical energy efficiency was investigated. Results draw that the overall electrical energy efficiency is relatively low and shows a downward trend. The eastern region has the best scores of electrical energy efficiency, followed by the central region and then the western region. Furthermore, the main associated determinants were investigated by panel Tobit regression model. It was found that the effect of industrial structure and economic opening degree on electrical energy efficiency is positive on the whole country level, whereas the effect of government intervention and urbanization is negative. From a regional perspective, there are great differences in the effect of each influencing factors. Some corresponding policy recommendations are given.

Environmental regulation is an important driving force of green technology innovation. In this paper, environmental policy instruments are classified into three categories: command-control, market-incentive and social-will. Based on the panel data of 30 provinces in China from 2010 to 2017, a fixed effect model and a panel threshold regression model are used to test the heterogeneous effects of different types of environmental policy instruments on the green technology innovation in China. The results show that: (1) Overall, China’s environmental policy instruments do not provide sufficient impetus for green technology innovation; (2) The impact of command-control environmental policy instruments on green technology innovation has a single threshold effect. When its intensity exceeds a certain threshold, green technology innovation is improved. The impact of market-incentive environmental policy instruments on the green technology innovation shows a double threshold effect, that is to say, only when its intensity maintained within a reasonable interval, can green technology innovation be promoted by it; (3) There is significant spatial difference in the impact of different types of environmental policy instruments on green technology innovation. In order to induce green technology innovation, it is necessary to formulate a combined and differentiated environmental policy system, while rationally adjusting the strength of different types of environmental policy instruments.

Decoupling energy consumption from economic growth is vital to the realization of sustainable development. To reduce energy demand, China has implemented an energy cap policy and pledged to limit total energy consumption to around 5000 million tons of standard coal equivalent by 2020 in the 13th Five-Year Plan (2016–2020). The prospect of achieving this target relies on the understanding of China’s energy growth pattern. To this end, we investigate the transformation of the growth pattern in China’s energy consumption from 2005 to 2015. We combine environmental input-output approach and structural decomposition analysis to study the factors that influence the changes in China’s energy consumption. Results show that energy consumption growth shifted from export-driven before the Financial Crisis (2005–2007) to investment-driven during the Financial Crisis (2007–2010) and to consumption-driven both in the Economic Recovery (2010–2012) and in the New Normal (2012–2015). Consumption volume was the major promoting factor of the growth in energy consumption, but it became less important over time. In contrast, energy intensity was the primary factor offsetting energy consumption growth, with varying contribution over the period 2005–2015. Correspondingly, we further discuss how relevant policies for each period contribute to shaping the growth pattern and give recommendations for energy policymakers.

Giving full play to carbon emission reduction of green credits is essential to achieve carbon neutrality. According to low-carbon pilot policies and the condition of industrial transfer, this paper first sorts those provinces into different research zones. The zones are as follows: (Ⅰ) the first and second batch of low-carbon municipalities and the first batch of pilot provinces (L1) and other provinces (L2) and (Ⅱ) strong industry transfer-out zone (STR), weak industry transfer-out zone (WTR), and industrial transfer-in area (TIR). Then, we employ a dynamic panel data model and systematic GMM (SYS-GMM) approach to empirically test the impact of green credit and nongreen credit on carbon emissions. Further, this paper analyzes how to coordinate two types of credits to achieve carbon neutrality. The results show that, first, at the national level, the nexus of green credit and carbon emissions with an inverted U-shaped curve and the current impact of green credit is still in the first half of the inverted U-shaped stage. The achievement of carbon neutrality is associated with the ratio structure of green credit to nongreen credit and the scale of green credit. Second, the achievement of carbon neutrality is with regional heterogeneity. The achievement of carbon neutrality is associated with the scale of green credit in L2 and TIR, but also with the ratio structure of nongreen credit to green credit in L2 and STR. However, the carbon neutralization effects with green credit are insignificant in L1 WTR. Finally, based on those conclusions, this paper puts forwards some suggestions to provide references for the policy formulation of green credits and carbon neutrality.

Public transportation is often considered as a green travel mode to alleviate the negative externalities such as traffic congestion and haze pollution generated from transport. However, is prioritizing urban public transportation actually conducive to haze emission reduction? In this study, considering special emphasis on the cumulative effect of haze, a dynamic panel model is constructed to analyze and quantify the impact of public transportation on haze pollution by using the data of 284 cities in China, and the heterogeneity of the impact in cities with different pollution levels is examined. Several interesting findings are derived from the empirical results. First, the development of urban public transportation can significantly alleviate urban haze pollution. Second, the haze reduction effect of public transportation in cities with different pollution levels is non-universal. Comparatively speaking, the haze reduction effect of public transportation in lightly polluted cities is more evident than that in heavily polluted cities. Therefore, in order to reduce haze pollution in a more effective manner, China should continue to promote urban public transportation priority strategy. Moreover, the government should also formulate differentiated traffic development strategies to effectively alleviate the urban traffic burdens.

With the reform of the power system, the retail electricity market in China has gradually been liberalized. The mechanism of freely selling electricity have been set up. To grab market share and increase profits, electricity retail companies have introduced a series of retail electricity price packages. To evaluate the applicability of these retail electricity price packages, an adaptive evaluation index system that takes into account the interests of both the power company and the users is first established. Furthermore, an integrated evaluation model that combines data envelopment analysis (DEA) and the cloud model is proposed. In this model, DEA is used to process quantitative indicators and the cloud model is employed to quantify qualitative problems. A case study of Tianjin is conducted to verify the effectiveness of the proposed evaluation system and model. The empirical study shows that qualitative indicators can also affect the applicability of the retail electricity price packages, and the applicability of the retail electricity price package was different in different seasons. Finally, several reliable suggestions on how to design retail electricity price packages are given based on the research. This study provides useful support for customers to choose the price package to increase the competitiveness of power selling companies and ultimately promote the reform of power selling.

The change of user load characteristic is affected by many factors, among which power policy factors have a greater impact on user load characteristic. Based on the Sigmoid function, this paper constructs a response characteristic model of users to time-of-use tariff policy, then analyzes the changes of indicators of load characteristic before and after the implementation of the time-of-use tariff policy based on the summer typical daily load characteristic curve of users in a certain area. The analysis shows that the implementation of the time-of-use tariff policy is conducive to reducing difference between peak and valley, as well as increasing the load rate.

With the deepening of the reform on the selling side of electricity, the selling company must strengthen the analysis on the electricity consumption of users and arrange the purchase and sale of electricity scheme scientifically so as to occupy the target selling electricity market and obtain the profit of selling electricity. Based on the big data background, the paper uses the k-means clustering method to divide the load curve of 2,498 residential power users into 5 categories. On the premise of considering the system load, the above five categories are classified into three types: peak load, partial peak load, and stable power users.

The linkages among carbon, renewable energy, and electricity markets are gradually strengthening. In order to prevent risk transmission among markets, this paper uses the TVP-VAR-DY (Time-Varying Parameter–Vector Auto Regression–Dynamic) model to analyze the dynamic risk spillover effects and network structure of risk transmission among carbon, renewable energy, and electricity markets. The empirical results show that there are significant asymmetric spillover effects among carbon, renewable energy, and electricity markets. The total spillover index shows that spillover effects among carbon, renewable energy, and electricity markets are time-varying, especially during unexpected events. Besides, the net spillover index indicates that the spillover effects are bidirectional, asymmetric, and time-varying. Finally, under the influence of unexpected events, the network structures of risk transmission among carbon, renewable energy, and electricity markets are heterogeneous. Compared to the Russia-Ukraine conflict, the COVID-19 pandemic has a more significant impact on these markets.

Power technology innovation has been positioned as an effective way to contribute to China’s carbon productivity. However, limited empirical evidence exists on the impact of power technology innovation on carbon productivity. Thus, based on the annual panel dataset of 30 China’s provinces from 2001 to 2019, this study explored whether and how power technology innovation promotes or impedes the improvement of carbon productivity. First, carbon productivity in the framework of total factor was calculated based on the metafrontier Malmquist-Luenberger productivity index. Second, the effect of power technology innovation on carbon productivity was investigated using the spatial Durbin model. And we also examined whether heterogeneous power technology innovations have a synergistic effect on carbon productivity. Third, influence mechanism of power technology innovation affecting carbon productivity was identified. Results show that (1) there are notable differences in China’s provincial carbon productivity, which is characterized by the spatial correlation. (2) Local power technology innovation has a promotion effect on carbon productivity in both local and neighboring provinces. Moreover, the promotion effect of breakthrough power technology innovation is stronger than that of incremental power technology innovation. (3) Catching-up Effect and Innovation Effect are important transmission channels through which power technology innovation improves carbon productivity. Finally, policy recommendations are provided.