{"title":"Effect of electricity policy uncertainty and carbon emission prices on electricity demand in China based on mixed-frequency data models","authors":"Wanbo Lu , Qibo Liu , Jie Wang","doi":"10.1016/j.jup.2024.101825","DOIUrl":null,"url":null,"abstract":"<div><div>This paper first constructs the electricity policy uncertainty (EPU) in China with textual methods and analyses the effect of the EPU and carbon emission prices (CEPs) on the total electricity demand. This paper also forecasts the demand for electricity in China with three mixed-frequency data models. The results show that the EPU index efficiently captures the uncertainty of China's electricity policy. The effects of EPU and CEPs on electricity demand are significant, and incorporating them into the forecasting model will improve the accuracy and timeliness. Moreover, compared with the ARMA model and LSTM neural networks, mixed-frequency data models perform better in electricity demand forecasting.</div></div>","PeriodicalId":23554,"journal":{"name":"Utilities Policy","volume":"91 ","pages":"Article 101825"},"PeriodicalIF":3.8000,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Utilities Policy","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0957178724001188","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
Abstract
This paper first constructs the electricity policy uncertainty (EPU) in China with textual methods and analyses the effect of the EPU and carbon emission prices (CEPs) on the total electricity demand. This paper also forecasts the demand for electricity in China with three mixed-frequency data models. The results show that the EPU index efficiently captures the uncertainty of China's electricity policy. The effects of EPU and CEPs on electricity demand are significant, and incorporating them into the forecasting model will improve the accuracy and timeliness. Moreover, compared with the ARMA model and LSTM neural networks, mixed-frequency data models perform better in electricity demand forecasting.
期刊介绍：
Utilities Policy is deliberately international, interdisciplinary, and intersectoral. Articles address utility trends and issues in both developed and developing economies. Authors and reviewers come from various disciplines, including economics, political science, sociology, law, finance, accounting, management, and engineering. Areas of focus include the utility and network industries providing essential electricity, natural gas, water and wastewater, solid waste, communications, broadband, postal, and public transportation services.
Utilities Policy invites submissions that apply various quantitative and qualitative methods. Contributions are welcome from both established and emerging scholars as well as accomplished practitioners. Interdisciplinary, comparative, and applied works are encouraged. Submissions to the journal should have a clear focus on governance, performance, and/or analysis of public utilities with an aim toward informing the policymaking process and providing recommendations as appropriate. Relevant topics and issues include but are not limited to industry structures and ownership, market design and dynamics, economic development, resource planning, system modeling, accounting and finance, infrastructure investment, supply and demand efficiency, strategic management and productivity, network operations and integration, supply chains, adaptation and flexibility, service-quality standards, benchmarking and metrics, benefit-cost analysis, behavior and incentives, pricing and demand response, economic and environmental regulation, regulatory performance and impact, restructuring and deregulation, and policy institutions.
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