Research On Electricity Consumption Behavior And Its Governance In Energy-intensive Industry

The energy-intensive industry confronting with serious obstructions, as long-term extensive production mode hard to reverse and huge consumption of primary energy in production along with tremendous negative externalities effects on economy, society and environment, is in a dilemma in the process of economic transition. Curbing the blindly expanding of energy-intensive industry is the desirability of public policy at the current period of serious unbalance in energy supply and demand, and exacerbating environment deterioration. The governance using tax rebate cancellation on import equipment for part of the industry especially the energy-intensive industry, as well as the differential electricity price charged on energy intensive industries to restrain the disorder in development, are all for the common goal as reducing the negative impact from production process in energy-intensive industries. However, it is the leading status in economy growth especially the driving force on national economy in economic recovery forming the dependency on energy-intensive industry in certain time period, that makes it’s difficult to control the energy consumption behavior by imposing the uniformity in all cases. It is the same sense from the perspective of governancing electricity consumption behavior in scientific and orderly way, either controlling the excessive occupation of electric energy by energy-intensive industry under the existing installed capacity and power construction ability, or recognizing the baseline of electric energy consumption to engage in and maintain basic economic production activities. Therefore, scientifically understanding the status and role of the energy-intensive industries in the aspect of electric energy consumption, systematically studying the electricity consumption, efficiency and related issues and developing effective countermeasures, has positive effects on norming electricity consumption behavior and transforming the electricity and energy consumption mode to an efficient and rational way, and also is of great significance on the economic growth mode transformation, economic structure strategic transformation and the sustainable development of energy, economy and environment. Based on the above background, this article targeted on electricity consumption behavior, based on the review of related former research, analyzed the condition of energy-intensive industry in China in-depth in order to providing useful reference in enhancing China’s electric energy utilization efficiency, forecasting the tendency of electricity consumption behavior of energy consuming entities, promoting the effect of electricity tariffs mechanism on specifying and guiding on electricity consumption behavior.The content and contribution is expressed as follows:Firstly, the concept of electricity consumption behavior of energy-intensive industry is defined. The analysis of essential characteristics of guiding ideology, mode, agent and strategy of electricity utilization puts forward innovative viewpoint that specifying the order of electricity consumption behavior being targeted on multi-agent is the key for governancing electricity consumption in order among energy-intensive industries, not merely the forecasting and control the developing tendency of quantity of electricity consumption or other unilateral aspects and objects.Secondly, the estimation of the electricity consumption behavior of energy intensive industry is studied through the concept of total factor energy efficiency for electricity and total factor energy efficiency change index for electricity. Based on the DEA method and Luenberger productivity index, the estimation model of these two indexes considering the concrete format of energy consumption is proposed and used for empirical analysis with industrial panel data between1985and2010. The estimated value of above indexes combined with electricity consumption of energy-intensive industry together, as representative measurement indicators of electricity consumption behavior.Thirdly, the assessment of the performance of the electricity consumption behavior in energy-intensive industries is applied to determine the annual class of electricity consumption behavior, based on historical data and the idea of governance classification. This kind of behaviour division on performance is refered to classification evaluation in grey theory.Fourthly, the expected performance of the electricity consumption behavior of energy-intensive industry is forecasted based on the classification results. With the forecast probability capacity of Logistic Regression Model and Cumulative Logistic Model as well as the learning and classifying ability on training samples of machine learning algorithm, the electricity consumption behavior is classified with forecasted index data from the perspective of internal behavior itself and external industrial developing environment. The results of machine learning algorithm also act as the manifest of probability forecasting results with Logistic Regression Model and Cumulative Logistic Model. The feasibility of this research method is also approved in this section.Fifthly, the governance issue and mechanism design of electricity consumption behavior is studied by punitive tariff increases mechanisms on electricity price with inversion incentive effects and constraint roles. From the input-output tables and improved input-output table, the variation data engaged in productive economic activities and social life derived from punitive tariffs on electricity price is obtained, with which the multi-objective optimization model of punitive tariff increases is established subjecting to the constraints of variation limitation of the consumer price index (CPI) and GDP reduction. And with the model of maximizing economic and social benefit, the punitive traiff on electricity price is estimated through optimizing algorithm and also the effect of punitive tariff with various ratios on electricity consumption behavior improvement is finally analyzed.