sustainability-logo

Journal Browser

Journal Browser

The Role of Digitalization and Artificial Intelligence in Low-Carbon Energy Transition and Achieving Carbon Neutrality: Interdisciplinary Perspectives

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Economic and Business Aspects of Sustainability".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 454

Special Issue Editors


E-Mail
Guest Editor
Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, China
Interests: energy system analysis and energy strategic planning, especially on low-carbon energy transition; sustainable development of energy and mineral resources; regional and technological energy economics
Special Issues, Collections and Topics in MDPI journals

E-Mail
Guest Editor
School of Management, Guilin University of Aerospace Technology, Guilin 541004, China
Interests: input–output analysis; environmental management; energy economics; digitalization and sustainable development
Special Issues, Collections and Topics in MDPI journals

E-Mail
Guest Editor
School of Management, Guilin University of Aerospace Technology, Guilin 541004, China
Interests: carbon peaking and carbon neutrality policy; sustainable development of energy and resources; the coupling of economic development, energy consumption, and carbon emissions; energy allocation analysis; research on mathematical theory of Sankey diagram
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Achieving a low-carbon energy transition and carbon neutrality is a complex, systematic endeavor that faces numerous challenges and uncertainties. The goals of carbon peaking and carbon neutrality have given rise to a series of significant interdisciplinary research issues. These encompass not only natural sciences and engineering technologies, such as digital innovations for carbon reduction and the construction of intelligent carbon sinks, but also economic and management concerns, including the optimization of industrial layout, the development of carbon trading and pricing mechanisms, the integration of carbon finance, international cooperation, and digital solutions for climate governance.

In this context, digitalization and intelligent systems play pivotal roles in reshaping low-carbon energy systems and enhancing carbon neutrality efforts. By incorporating data-driven management theories and methodologies, the transition to a low-carbon economy can be supported in several key ways. First, intelligent systems and digital technologies can be leveraged to identify optimal technical pathways, prioritize pollutant and carbon emission reductions, and construct dynamic, real-time carbon emission indicators for industries and regions. Second, digitalization fosters innovation in low-carbon business models and promotes the adoption of sustainable consumption patterns by integrating micro-level entities such as enterprises and individuals into the carbon neutrality process. Third, intelligent systems enable the design of more efficient and transparent carbon markets by enhancing pricing models and facilitating the optimal allocation of carbon emission rights through data analytics and algorithmic solutions.

It is imperative to conduct systematic and in-depth research on how digitalization and intelligence can address the critical challenges of low-carbon energy transition and carbon neutrality. By uncovering the underlying principles and developing new interdisciplinary methodologies, such research can not only advance theoretical understanding but also provide actionable insights for policy and industry.

This Special Issue focuses on the interdisciplinary exploration of digitalization and intelligence in low-carbon energy transitions. We welcome original research articles and review papers, particularly those that present novel theories, methodologies, findings, and perspectives. For empirical studies, we encourage authors to provide comprehensive case contexts, a robust interpretation of results, comparative analyses with similar studies, and discussions on the generalizability of their conclusions. Articles that integrate insights from multiple disciplines, highlight the transformative role of digital and intelligent systems, and offer practical implications for achieving carbon neutrality will receive special consideration.

Research areas may include (but are not limited to) the following:

  • The role of digitalization and intelligent systems in achieving carbon neutrality: exploring how technologies like big data, artificial intelligence, and IoT can optimize energy systems and drive low-carbon innovation.
  • Energy policy and strategic pathways under the carbon neutrality framework: studies focusing on policies and strategies that facilitate low-carbon transitions at local, regional, and national levels.
  • Coupling research of economic development, energy consumption, and carbon emissions: investigations into the dynamic relationships among economic growth, energy utilization, and emission patterns, with interdisciplinary approaches.
  • Low-carbon energy systems and digital solutions at multiple scales: research addressing the integration of digital technologies in building sustainable energy systems across different geographic or industrial contexts (e.g., cities, industries, or rural areas).
  • Behavioral impacts on carbon neutrality: analyzing how organizational or individual energy-saving behaviors and low-carbon lifestyles contribute to achieving carbon neutrality.
  • Cultivation of “carbon-related talents” in the digital era: examining innovative education models and skill frameworks for developing talent to meet the demands of a carbon-neutral and intelligent society.
  • The implementation path of carbon neutrality in key industries: case studies and strategies for carbon-neutral transitions in sectors such as tourism, transportation, or manufacturing.
  • Regional studies on carbon neutrality in ASEAN countries: insights into the unique challenges, opportunities, and policy frameworks for achieving carbon neutrality in ASEAN nations, including the role of digital and intelligent solutions.

We look forward to receiving your contributions.

The Journal Sustainability is indexed within Scopus, SCIE and SSCI (Web of Science), GEOBASE, GeoRef, Inspec, AGRIS, RePEc, CAPlus / SciFinder, and other databases.

Dr. Linwei Ma
Dr. Xiaoyong Zhou
Dr. Chin Hao Chong
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. Sustainability 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 2400 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

  • carbon peaking
  • carbon neutrality
  • CO2 emissions
  • energy
  • management science
  • sustainable energy systems
  • low-carbon
  • economy development
  • energy systems

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

23 pages, 2216 KiB  
Article
AI vs. ESG? Uncovering a Bidirectional Struggle in China’s Sustainable Finance
by Zizhe Du and Chao Chen
Sustainability 2025, 17(9), 4238; https://doi.org/10.3390/su17094238 - 7 May 2025
Viewed by 238
Abstract
As global discourse increasingly centers on environmental, social, and governance considerations, ESG investment has become a major trend in financial markets. Artificial intelligence (AI), through its rapid evolution, has exerted a transformative influence that continues to reshape the fundamental structures of this domain. [...] Read more.
As global discourse increasingly centers on environmental, social, and governance considerations, ESG investment has become a major trend in financial markets. Artificial intelligence (AI), through its rapid evolution, has exerted a transformative influence that continues to reshape the fundamental structures of this domain. This study investigates the dynamic relationship between AI and ESG investment indices in China, aiming to reveal the bidirectional causal linkages and time-dependent interactions between these two critical areas. In methods, we used four different parameter stability tests to indicate that the Granger causality test based on the full-sample VAR model may produce biased results. Therefore, we employed a bootstrap rolling-window subsample Granger causality test using data from January 2013 to September 2024 in China. The results reveal a significant dynamic relationship between ESG investment and AI. In key findings, we find that AI exerts a negative impact on ESG investment. AI development attracts substantial capital inflows that favor technological advancement and commercialization over long-term ESG investments. Meanwhile, ESG investment shows both positive and negative effects on AI. The positive effect indicates that ESG investment promotes AI research and applications emphasizing energy efficiency, data privacy, and fairness, thereby supporting the sustainable development of AI technologies. However, driven by short-term economic returns, strict ESG standards and compliance requirements may, in the short term, constrain the development of certain energy-intensive or emerging AI technologies. In economic and political implications, our study provides policymakers with scientific evidence to improve the ESG investment environment and to design balanced policies that support both AI development and sustainable investment practices. It underscores the necessity of promoting coordinated development between AI and ESG investment to achieve global sustainability goals and recommends measures to align short-term economic interests with long-term ESG objectives. This study is expected to serve as a scientific basis for ESG goal-setting and contribute to the realization of China’s dual-carbon goals. In particular, it facilitates the convergence of artificial intelligence technologies with sustainable development initiatives and tells the importance of responsible technological progress for global sustainable development. Full article
Show Figures

Figure 1

Back to TopTop