• Energy Research

Abstract To realize the “Green” economic development, China has to transform the extensive mode of growth with the power factor inputs being the main driving force gradually into an intensive one. It's important to quantitatively analyze the relationship between the power factor input and economic growth and understand the core factors that affect the coordinated development between them. We introduce the concept of “power dependence,” based on the counter-factual method, use “growth drag” to reflect the dependence of China's economic growth on the power factor input. Based on the measurement of national power dependence, the auto-regressive distributed lag (ARDL) model is used to develop an empirical analysis of its influencing factors, and the robustness of the findings is tested by using the inter-provincial panel data. The findings show that China's economic growth has depended on the power factor input to some extent. According to the results, we propose suggestions from the aspects of promoting economic development, improving the pricing mechanism, adjusting the power generation structure, reasonably regulating the introduction of the foreign direct investment and research and development investment, and optimizing industrial structure to find an effective means to reduce the dependence of China's economic growth on the power factor input.

Investigating the factors influencing the spatial-temporal disparities in China’s electricity consumption carbon emissions (ECCEs) will be of great help to advancing the reduction in carbon emissions on the consumption side of electricity. Based on the measurement of the ECCEs in 30 Chinese provinces between 2005 and 2021, we utilized the natural breakpoint method and the Dagum Gini coefficient to analyze the spatial-temporal disparities in ECCEs at the provincial and regional levels, and then we used Geodetector to explore the factors influencing the spatial-temporal disparities in ECCEs. The results revealed the following: (1) There were obvious inter-provincial spatial disparities in ECCEs, with coastal provinces such as Jiangsu and Guangdong consistently ranking at the top of the country and inland provinces such as Qinghai and Yunnan having relatively low carbon emission values. (2) The overall disparities in China’s ECCEs fluctuated and rose, with inter-regional disparities being the primary source of the overall disparities. (3) Economic development, industrialization level, population density, and foreign direct investment all had strong explanations for the spatial-temporal disparities in China’s ECCEs. When all these influencing factors were spatially superimposed, their effects were enhanced.

Abstract China has proposed the Renewable Portfolio Standard (RPS) policy to advance the stable development of renewable energy. The RPS requires each province to achieve a stated minimum share of renewable energy power in the total provincial power generation. However, there is an obvious mismatch between the actual capability of generating renewable power and the assigned responsibility for the share of renewable energy power based on the RPS in some provinces. Therefore, this study aims to optimize the renewable power dispatching strategy across provinces for satisfying the RPS requirements in China and to assess the corresponding pressure for each province. A renewable energy power dispatching model is developed, and an economically feasible strategy for dispatching renewable energy power in Chinese provinces in 2020–2022 was obtained. The results indicate that it is necessary to dispatch 395.2 and 140.4 TWh of hydropower and non-hydropower nationwide, respectively, in 2022 to fulfill the RPS target when the COVID-19 is effectively controlled worldwide. If COVID-19 cannot be effectively controlled, 376.6 and 127.8 TWh of hydropower and non-hydropower must be dispatched nationwide to fill the gap. Beijing, Tianjin, Shanghai, and Zhejiang are faced with a relatively high pressure under the RPS target. Finally, a path for each province to achieve its RPS target is proposed.

Abstract Smart home, is expected to bring great changes to people's lifestyles. By shifting the timing of residents' electricity consumption, smart home can improve the flexibility of the power load, and provide significant potential for power demand responses. These responses can substantially mitigate peak-to-valley power demand gaps and household electricity costs. However, the extent of the likely impacts from smart home participating in power demand response remains unknown, and very limited research has been conducted thereon. Therefore, this study attempts to explore the potential changes in peak-to-valley electricity consumption and electricity costs owing to smart home, by developing a multi-objective smart home integrated management model with the consideration of appliances and household electricity consumption behavioral heterogeneity. The survey data collected in China was employed in the empirical analysis. Results show that smart home participating in power demand response can reduce peak load by 29.3%–49.3%, which is up to 149 GW, and the peak-to-valley difference could be decreased by 37.5%–78.2%. However, significant variance exists for the smart home impacts among households with different structures and individual occupations. Teachers, freelancers, and homeworkers contribute more to this reduction. In addition, the peak-to-valley difference after introducing smart home would shrink from −80.7%–-68.5% to −29.2%–23.9% for areas with the time-of-use price policy, which performs better than the areas without time-of-use price policy. Regarding the economic benefits, smart home participating the demand response could reduce the investments in the power supply and power grid by 1.13–1.19 trillion RMB in China.