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Exploring the Role of Electricity Big Data in Achieving the Carbon Neutrality Target

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "C: Energy Economics and Policy".

Deadline for manuscript submissions: closed (31 July 2024) | Viewed by 3907

Special Issue Editors

Dr. Hao Chen

E-Mail Website
Guest Editor
School of Applied Economics, Renmin University of China, Beijing 100872, China
Interests: electricity economics; climate change economics
Dr. Chengzhu Gong

E-Mail Website
Guest Editor
School of Economics and Management, China University of Geosciences, Wuhan 430074, China
Interests: energy market modelling; energy price reform; energy policy
Dr. Rui Yan

E-Mail Website
Guest Editor
School of Economics and Management, University of Science & Technology Beijing, Beijing 100083, China
Interests: energy and environment policy; system optimization modeling; supply chain and logistics management
Dr. Hongda Gao

E-Mail Website
Guest Editor
School of Modern Post (School of Automation), Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: complex systems modelling; energy and electricity economics

Special Issue Information

Dear Colleagues,

To address climate change and achieve sustainable development, many countries have committed to the process of achieving carbon neutrality before 2060. To successfully accomplish this target, a coordinated effort from all the relevant stakeholders will be required in the long term. To ameliorate climate policies, it is urgent to monitor and track carbon emissions of different enterprises, thus dynamically guiding, calibrating, and regulating the carbon emissions of enterprises to be in line with climate goals. Characterized by high frequency, wide coverage and rich information, electricity big data is an important resource in helping to achieve the carbon neutrality targets, such as carbon emission monitoring, carbon asset management and carbon emission reductions, etc.

This Special Issue seeks to publish novel research and reflect the most recent advances on the latest contributions on the above areas, covering new modeling techniques and formulations, as well as innovative case studies. Contributions will be selected through a refereeing process consistent with the standard reviewing process of the Energies journal. This Special Issue should be of benefit to policymakers and researchers worldwide, being applicable to any area where there is a need to overcome challenges, and design policy options and market mechanisms that will promote a new era of carbon neutrality.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Carbon emission monitoring using electricity big data.
  • Tracking the carbon neutrality progress and gap using electricity big data.
  • Long-term carbon emission path simulation with electricity big data
  • Carbon emission reduction strategies using electricity big data.
  • Electricity big data and carbon asset management
  • The impacts of climate change on electricity consumption behavior

Dr. Hao Chen
Dr. Chengzhu Gong
Dr. Rui Yan
Dr. Hongda Gao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • electricity big data
  • carbon neutrality
  • monitor
  • carbon asset
  • strategy
  • optimization

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Published Papers (3 papers)

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Research

16 pages, 2546 KiB  
Article
A Big Data-Driven Approach for Early Warning of Enterprise Emissions Alignment with Carbon Neutrality Targets: A Case Study of Guangxi Province
by Chunli Zhou, Huizhen Tang, Wenfeng Zhang, Jiayi Qiao and Qideng Luo
Energies 2024, 17(11), 2508; https://doi.org/10.3390/en17112508 - 23 May 2024
Viewed by 539
Abstract
Achieving the target of carbon neutrality has been an important approach for China to mitigate global climate change. Enterprises are major carbon emitters, and a well-designed early warning system is needed to ensure that their emissions align with carbon neutrality goals. Therefore, this [...] Read more.
Achieving the target of carbon neutrality has been an important approach for China to mitigate global climate change. Enterprises are major carbon emitters, and a well-designed early warning system is needed to ensure that their emissions align with carbon neutrality goals. Therefore, this study utilized electricity big data to construct an early warning model for enterprise carbon emissions based on carbon quota allocation. Taking key carbon-emitting enterprises in Guangxi as a case study, we aim to provide insights to support China’s dual carbon goals. Firstly, we established the Carbon Quota Allocation System, enabling carbon quota allocation at the enterprise levels. Secondly, we developed the Enterprise Carbon Neutrality Index, facilitating dynamic warnings for carbon emissions among enterprises. The main conclusions are as follows: (1) In 2020, Guangdong received the highest carbon quota of 606 million tons, representing 5.72% of the national total, while Guangxi only received 2.63 billion tons. (2) Only 39.34% of enterprises in Guangxi are able to meet the carbon neutrality target, indicating significant emission reduction pressure faced by enterprises in the region. (3) Over 90% of enterprises in Guangxi receive Commendation and Encouragement warning levels, suggesting that enterprises in Guangxi are demonstrating a promising trend in emission reduction efforts. Full article
(This article belongs to the Special Issue Exploring the Role of Electricity Big Data in Achieving the Carbon Neutrality Target)
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19 pages, 7186 KiB  
Article
Tracking the Carbon Emissions Using Electricity Big Data: A Case Study of the Metal Smelting Industry
by Chunli Zhou, Yuze Tang, Deyan Zhu and Zhiwei Cui
Energies 2024, 17(3), 652; https://doi.org/10.3390/en17030652 - 30 Jan 2024
Viewed by 1044
Abstract
Implementing real-time carbon emissions monitoring at the enterprise level enables the detailed breakdown of carbon neutrality goals for microcosmic enterprises, which is of paramount significance in ensuring the precision of policy formulations. Grounded in China’s historical electricity consumption and carbon emissions data, this [...] Read more.
Implementing real-time carbon emissions monitoring at the enterprise level enables the detailed breakdown of carbon neutrality goals for microcosmic enterprises, which is of paramount significance in ensuring the precision of policy formulations. Grounded in China’s historical electricity consumption and carbon emissions data, this study utilizes the network approach and input–output methods to compute and predict direct and indirect transmission coefficients of electricity consumption and carbon emissions in each province. We establish a methodology that enables the monitoring of real-time carbon emissions of enterprises based on corporate electricity consumption data. Using the metal smelting industry in Guangxi as an example, our findings are as follows: First, in 2020, the comprehensive carbon emissions of the ferrous metal smelting industry in Guangxi reached 58.84 million tons, marking a notable increase of 42.78% compared to emissions in 2014, indicating that emissions reductions are imperative. Second, significant regional variations in emission coefficients are observed, with values ranging from 14 g CO2/KWh to 940 g CO2/KWh among provinces. Meanwhile, the trends of direct carbon emissions and indirect carbon emissions are totally different, underscoring the importance of comprehensive carbon accounting in informing policy decisions. Third, through the carbon emissions real-time monitoring of 75 metal smelting industry enterprises using electricity big data, we identified that the distribution of emissions across industries, time periods, and regions is uneven. Overall, this method can optimize carbon emission measurement techniques to a higher spatiotemporal resolution and more microscopic monitoring subjects, providing essential numerical foundations for promoting carbon emissions reduction and sustainable development. Full article
(This article belongs to the Special Issue Exploring the Role of Electricity Big Data in Achieving the Carbon Neutrality Target)
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19 pages, 4869 KiB  
Article
Real-Time Carbon Emissions Monitoring of High-Energy-Consumption Enterprises in Guangxi Based on Electricity Big Data
by Chunli Zhou, Xiqiao Lin, Renhao Wang and Bowei Song
Energies 2023, 16(13), 5124; https://doi.org/10.3390/en16135124 - 3 Jul 2023
Cited by 10 | Viewed by 1681
Abstract
Real-time carbon emissions monitoring at the enterprise level is a crucial tool in shifting macrolevel carbon peak and carbon neutrality plans toward micro-level implementations. This study extends the existing CO2 emissions accounting framework to enterprise emissions monitoring. We analyze the correlation mechanism [...] Read more.
Real-time carbon emissions monitoring at the enterprise level is a crucial tool in shifting macrolevel carbon peak and carbon neutrality plans toward micro-level implementations. This study extends the existing CO2 emissions accounting framework to enterprise emissions monitoring. We analyze the correlation mechanism between electricity consumption and CO2 emissions by industries, calculate the electricity–CO2 coefficients, and finally model an enterprise-level real-time carbon emissions monitoring method based on electricity big data. Taking Guangxi region as a sample, the results show that (1) the proportion of electricity-related emissions is on the rising stage in Guangxi, with 441 g CO2/KWh emitted from electricity consumption in 2020, (2) the carbon emissions from the energy-intensive industries account for over 70% of the whole society, and they all have high electricity–CO2 coefficients, far exceeding the industry average of 1129 g/kWh, and (3) the monitoring method is applied to 1338 enterprises from over 40 industries. The emission characteristics reflect the regional and industrial heterogeneity. This enterprise-level monitoring method aims to optimize the carbon emissions calculation method toward higher temporal and spatial resolutions, so as to provide an important numerical basis for promoting carbon emission reduction and sustainable development. Full article
(This article belongs to the Special Issue Exploring the Role of Electricity Big Data in Achieving the Carbon Neutrality Target)
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