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Sustainability
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Carbon Neutrality and Green Development
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Carbon Neutrality and Green Development

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 December 2025 | Viewed by 11747

Special Issue Editors

Prof. Dr. Shihong Zeng

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Guest Editor
College of Economics & Management, Beijing University of Technology, Beijing 100124, China
Interests: carbon neutrality; green development
Special Issues, Collections and Topics in MDPI journals
Dr. Qiao Peng

E-Mail Website
Guest Editor
Queen's Business School (QBS), Queen's University, Belfast BT71NN, UK
Interests: carbon finance; energy management; electricity market analysis; transportation planning

Special Issue Information

Dear Colleagues,

The pursuit of carbon neutrality is poised to profoundly influence the sustainable development trajectories of economies and industries worldwide, spanning both developed and developing nations. Over the next three decades, a plethora of issues will demand rigorous examination and deliberation, offering insights valuable to policymakers and investors alike. This Special Issue seeks to serve as a platform for global scholars to exchange perspectives on this imperative, aiming to drive contributions toward fostering sustainable development on a global scale.

The call for papers covers various topics that include, but are not limited to, green economic growth, carbon finance, energy finance, climate finance, green finance, green bonds, and environmental finance. Other areas of interest include, among others:

  • The impact of carbon neutrality goals on long-term and short-term economic growth;
  • The impact of carbon neutrality goals on the cost of economic transformation;
  • The optimization and policy simulation of urban energy structures towards carbon neutrality goals;
  • The impact of green technological innovation on energy intensity;
  • Carbon information disclosure standards;
  • The impact of carbon information disclosure on corporate financing;
  • The impact of carbon information disclosure on corporate performance;
  • The quality of carbon information disclosure;
  • Urban construction and carbon neutrality;
  • Regional economies and carbon neutrality;
  • Urban economies and carbon neutrality;
  • Low-carbon buildings and carbon neutrality;
  • The impact of low-carbon or zero-carbon materials and the circular economy on achieving carbon neutrality;
  • Artificial intelligence and carbon neutrality;
  • Bitcoin and carbon neutrality;
  • The digital economy and carbon neutrality.

Prof. Dr. Shihong Zeng
Dr. Qiao Peng
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 neutrality goals
  • green development
  • carbon finance
  • economic transformation
  • energy structure
  • carbon information disclosure

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Published Papers (9 papers)

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Research

18 pages, 678 KiB  
Article
Can Carbon Neutrality Promote Green and Sustainable Urban Development from an Environmental Sociology Perspective? Evidence from China
by Yujing Pan and Yifei Zhou
Sustainability 2025, 17(9), 4209; https://doi.org/10.3390/su17094209 - 7 May 2025
Viewed by 234
Abstract
Against the backdrop of global climate change and rapid urbanisation, carbon-neutral urban governance and sustainable urban development have become core issues of concern to the international community. As the world’s largest carbon emitter, Chinese cities shoulder the significant responsibility of achieving the “dual-carbon” [...] Read more.
Against the backdrop of global climate change and rapid urbanisation, carbon-neutral urban governance and sustainable urban development have become core issues of concern to the international community. As the world’s largest carbon emitter, Chinese cities shoulder the significant responsibility of achieving the “dual-carbon” goal. This study utilised a unique panel dataset of 300 cities in China from 2015 to 2022 and proposed a multi-dimensional analytical framework from the perspective of environmental sociology. This paper empirically examines the impact mechanism of carbon-neutral governance on urban sustainable development and its regional heterogeneity by using this framework. The research findings are as follows: First, carbon-neutral governance has a significant promoting effect on the sustainable development of cities. Secondly, technological input (the number of scientific researchers) plays a significant mediating role between carbon-neutral governance and sustainable development, indicating that technology diffusion is an important way for the transmission of policy effects. Thirdly, the analysis of regional heterogeneity indicates that due to policy inclination and resource concentration, western cities contribute the most to sustainable development, followed by eastern cities, and central cities contribute the least to sustainable development. The eastern region was identified as the second weakest and the central region as the weakest. This research provides theoretical and empirical basis for differentiated formulation of carbon neutrality policies, strengthening scientific and technological support, and optimising regional collaborative governance. Full article
(This article belongs to the Special Issue Carbon Neutrality and Green Development)
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33 pages, 3775 KiB  
Article
Renewable Investments, Environmental Spending, and Emissions in Eastern Europe: A Spatial-Economic Analysis of Management and Policy Decisions Efficiency
by Bogdan Nichifor, Luminita Zait and Ovidiu Turcu
Sustainability 2025, 17(7), 3010; https://doi.org/10.3390/su17073010 - 28 Mar 2025
Viewed by 548
Abstract
The transition to a low-carbon economy is a key challenge for Eastern Europe, where economic growth, energy investments, and emission reduction policies interact in complex ways. This study employs a spatial econometric approach to assess the effectiveness of renewable energy investments and government [...] Read more.
The transition to a low-carbon economy is a key challenge for Eastern Europe, where economic growth, energy investments, and emission reduction policies interact in complex ways. This study employs a spatial econometric approach to assess the effectiveness of renewable energy investments and government environmental spending in mitigating CO2 emissions across the region. Using panel data and spatial Durbin models (SDMs), we identify significant spillover effects in emissions reduction, revealing those environmental policies in one country influence neighboring regions. The results indicate that renewable investments have a positive but localized impact on emissions reduction, whereas government environmental expenditure exhibits diminishing returns beyond a threshold of 0.01 GDP. Threshold regression analysis confirms that excessive spending may lead to inefficiencies, reversing its expected benefits. Additionally, stochastic frontier analysis (SFA) highlights disparities in energy efficiency, with some countries demonstrating stronger optimization strategies than others. These findings underscore the importance of policy coordination and targeted investment strategies to enhance the effectiveness of decarbonization efforts. Strengthening regional cooperation and optimizing environmental expenditure allocation can significantly improve sustainability outcomes in Eastern Europe. Full article
(This article belongs to the Special Issue Carbon Neutrality and Green Development)
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23 pages, 727 KiB  
Article
Research on the Green and Low-Carbon Development Path of Digital Intelligence Empowering Enterprises in Manufacturing Industry
by Xiaofei Wang, Shaowen Zhan, Longlong Liu and Peng Zhang
Sustainability 2025, 17(6), 2734; https://doi.org/10.3390/su17062734 - 19 Mar 2025
Viewed by 573
Abstract
The Fourth Industrial Revolution, driven by advancements in information technology, has ushered humanity into the age of intelligence. As digital technologies like artificial intelligence and large-scale models continue to evolve and gain traction, the convergence of digital innovation and green development within manufacturing [...] Read more.
The Fourth Industrial Revolution, driven by advancements in information technology, has ushered humanity into the age of intelligence. As digital technologies like artificial intelligence and large-scale models continue to evolve and gain traction, the convergence of digital innovation and green development within manufacturing enterprises has emerged as a pivotal trend. This integration not only fosters high-quality, sustainable growth, but also increasingly validates the impact of digital intelligence on advancing low-carbon performance. This study delves into how manufacturing enterprises can attain sustainable and low-carbon growth via digital transformation, employing the entropy TOPSIS evaluation model to assess the effectiveness of various empowerment strategies. Based on the findings, the paper offers actionable recommendations for enhancing sustainable practices in manufacturing during this digital shift. Beyond enriching the theoretical framework on the synergy between digital intelligence and sustainability in manufacturing, this research provides practical insights and guidance for enterprises leveraging next-generation digital technologies to drive their green and low-carbon initiatives more effectively. Full article
(This article belongs to the Special Issue Carbon Neutrality and Green Development)
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25 pages, 1571 KiB  
Article
Exploring the Mechanisms and Pathways Through Which the Digital Transformation of Manufacturing Enterprises Enhances Green and Low-Carbon Performance Under the “Dual Carbon” Goals
by Jun Liu, Peng Zhang and Xiaofei Wang
Sustainability 2025, 17(3), 1162; https://doi.org/10.3390/su17031162 - 31 Jan 2025
Cited by 3 | Viewed by 1190
Abstract
The coordinated development of digitalization and greening is essential for economic transformation and upgrading, especially given the pressing global carbon emission challenges. China’s commitment to achieving “dual carbon” goals highlights the need for sustainable solutions, particularly in the manufacturing sector, which is a [...] Read more.
The coordinated development of digitalization and greening is essential for economic transformation and upgrading, especially given the pressing global carbon emission challenges. China’s commitment to achieving “dual carbon” goals highlights the need for sustainable solutions, particularly in the manufacturing sector, which is a significant source of energy consumption and emissions; carbon emissions account for more than 30%. Integrating advanced digital technologies with manufacturing is critical for reducing carbon and sustainable growth. According to the research results, more than 70% of scholars believe that digital transformation boosts green innovation and low-carbon development, but the mechanisms still need to be clarified, slowing transformation efforts and reducing efficiency. Taking the intellectualization and green low-carbon development of manufacturing enterprises as latent variables, and taking the nine paths obtained by scholars’ research results and investigation interviews to promote green low-carbon performance as observation variables, this paper constructs a structural equation model and deeply explores the mechanism and paths of the intellectualization transformation of manufacturing enterprises affecting carbon reduction, emission reduction and sustainable development of enterprises. The research results show that the digital intelligent transformation of manufacturing enterprises affects the green and low-carbon performance improvement and sustainable development of enterprises through technological innovation, industrial structure transformation and upgrading, and reshaping resource allocation. These strategies lower energy use and emissions, strengthen sustainability, and improve green performance. The findings offer theoretical and practical insights, providing a roadmap for efficient digital transformation in manufacturing to achieve the “dual carbon” goals and support sustainable development. Full article
(This article belongs to the Special Issue Carbon Neutrality and Green Development)
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27 pages, 2410 KiB  
Article
Research on Evolutionary Path of Land Development System Towards Carbon Neutrality
by Cong Xu, Liying Shen and Tso-Yu Lin
Sustainability 2025, 17(3), 1099; https://doi.org/10.3390/su17031099 - 29 Jan 2025
Viewed by 783
Abstract
Based on complex system theory and multi-dimensional coupling analysis paradigm, this study constructs a dynamic model covering land use, real estate development, and carbon emissions, and deeply explores the internal mechanism and evolution law of land development system in the process of moving [...] Read more.
Based on complex system theory and multi-dimensional coupling analysis paradigm, this study constructs a dynamic model covering land use, real estate development, and carbon emissions, and deeply explores the internal mechanism and evolution law of land development system in the process of moving toward a low-carbon path. Firstly, through nonlinear dynamics and bifurcation analysis, this study identifies three typical transformation paths that the system may experience: gradual, transitional, and hybrid, emphasizing the nonlinear, phased, and highly context-dependent characteristics of the transformation process. On this basis, early warning indicators and robustness analysis methods are introduced, which provide operational tools for identifying critical turning points in the system and improving the effectiveness and resilience of regulatory strategies. Furthermore, this paper proposes a multi-level regulation mechanism design framework, which combines the immediate feedback with the historical cumulative effect to achieve the refined guidance of land development patterns and carbon emission paths. The results provide a scientific basis and practical enlightenment for land use optimization, green infrastructure construction, and industrial structure adjustment under the background of realizing the “3060” dual carbon goal and the reform of territorial spatial planning in China. In the future, it is necessary to strengthen the empirical calibration of parameters, data-driven optimization, and collaborative research of multiple policy tools to further improve the applicability and decision-making reference value of the model. Full article
(This article belongs to the Special Issue Carbon Neutrality and Green Development)
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26 pages, 3822 KiB  
Article
Construction of a Corporate Carbon Disclosure Indicator System and Quality Evaluation: Evidence from Resource-Based Listed Companies
by Tian Li, Shihong Zeng, Shaomin Wu and Qiao Peng
Sustainability 2025, 17(1), 100; https://doi.org/10.3390/su17010100 - 27 Dec 2024
Cited by 1 | Viewed by 1330
Abstract
Resource-based companies are key players in reducing carbon emissions and play a central role in achieving China’s dual-carbon goal. Establishing and improving an objective carbon information disclosure mechanism for companies and evaluating the quality of carbon information disclosure in a scientific and reasonable [...] Read more.
Resource-based companies are key players in reducing carbon emissions and play a central role in achieving China’s dual-carbon goal. Establishing and improving an objective carbon information disclosure mechanism for companies and evaluating the quality of carbon information disclosure in a scientific and reasonable manner have significant reference value for rationally shaping the way to realize carbon peak and carbon neutrality. In view of this, this paper develops an evaluation index system based on four dimensions based on the corporate social responsibility reports of listed companies from 2018 to 2022. After excluding firms with a high degree of greenwashing, the combined weighting-TOPSIS method was used to evaluate the carbon disclosure quality of companies. The research results show that, although the quality of carbon disclosure of resource-based companies has indeed improved since the 2020 dual-carbon goal was proposed, there are differences in the quality of carbon disclosure of companies between different subsectors and regions, and relevant policy recommendations are proposed. Full article
(This article belongs to the Special Issue Carbon Neutrality and Green Development)
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25 pages, 2402 KiB  
Article
The Impact of Weather Variability on Renewable Energy Consumption: Insights from Explainable Machine Learning Models
by Rong Qu, Ruibing Kou and Tianyi Zhang
Sustainability 2025, 17(1), 87; https://doi.org/10.3390/su17010087 - 26 Dec 2024
Cited by 1 | Viewed by 2242
Abstract
The pursuit of carbon neutrality is reshaping global energy systems, making the transition to renewable energy critical for mitigating climate change. However, unstable weather conditions continue to challenge energy consumption stability and grid reliability. This study investigates the effectiveness of various machine learning [...] Read more.
The pursuit of carbon neutrality is reshaping global energy systems, making the transition to renewable energy critical for mitigating climate change. However, unstable weather conditions continue to challenge energy consumption stability and grid reliability. This study investigates the effectiveness of various machine learning (ML) models at predicting energy consumption differences and employs the SHapley Additive Explanations (SHAP) interpretability tool to quantify the influence of key weather variables, using five years of data (2017–2022) and 196,776 observations collected across Europe. The dataset consists of hourly weather and energy consumption records, and key variables such as Global Horizontal Irradiance (GHI), sunlight duration, day length, cloud cover, and humidity are identified as critical predictors. The results demonstrate that the Random Forest (RF) model achieves the highest accuracy and stability (R2 = 0.92, RMSE = 360.17, MAE = 208.84), outperforming other models in predicting energy consumption differences. Through SHAP analysis, this study demonstrates the profound influence of GHI, which exhibits a correlation coefficient of 0.88 with energy consumption variance. Incorporating advanced data preprocessing and predictor selection techniques remains the RMSE of RF but reduces the RMSE by approximately 25% for the XGBoost model, underlining the importance of selecting appropriate input variables. Hyperparameter tuning further enhances model performance, particularly for less robust algorithms prone to overfitting. The study reveals the complex seasonal and regional effects of weather conditions on energy demands. These findings underscore the effectiveness of ML models at addressing the challenges of complex energy systems and provide valuable insights for policymakers and practitioners to optimize energy management strategies, integrate renewable energy sources, and achieve sustainable development objectives. Full article
(This article belongs to the Special Issue Carbon Neutrality and Green Development)
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18 pages, 2126 KiB  
Article
Towards Carbon Neutrality: Machine Learning Analysis of Vehicle Emissions in Canada
by Xiaoxu Guo, Ruibing Kou and Xiang He
Sustainability 2024, 16(23), 10526; https://doi.org/10.3390/su162310526 - 30 Nov 2024
Viewed by 1569
Abstract
The transportation sector is a major contributor to carbon dioxide (CO2) emissions in Canada, making the accurate forecasting of CO2 emissions critical as part of the global push toward carbon neutrality. This study employs interpretable machine learning techniques to predict [...] Read more.
The transportation sector is a major contributor to carbon dioxide (CO2) emissions in Canada, making the accurate forecasting of CO2 emissions critical as part of the global push toward carbon neutrality. This study employs interpretable machine learning techniques to predict vehicle CO2 emissions in Canada from 1995 to 2022. Algorithms including K-Nearest Neighbors, Support Vector Regression, Gradient Boosting Machine, Decision Tree, Random Forest, and Lasso Regression were utilized. The Gradient Boosting Machine delivered the best performance, achieving the highest R-squared value (0.9973) and the lowest Root Mean Squared Error (3.3633). To enhance the model interpretability, the SHapley Additive exPlanations (SHAP) and Accumulated Local Effects methods were used to identify key contributing factors, including fuel consumption (city/highway), ethanol (E85), and diesel. These findings provide critical insights for policymakers, underscoring the need for promoting renewable energy, tightening fuel emission standards, and decoupling carbon emissions from economic growth to foster sustainable development. This study contributes to broader discussions on achieving carbon neutrality and the necessary transformations within the transportation sector. Full article
(This article belongs to the Special Issue Carbon Neutrality and Green Development)
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19 pages, 494 KiB  
Article
Research on Whether Artificial Intelligence Affects Industrial Carbon Emission Intensity Based on the Perspective of Industrial Structure and Government Intervention
by Ping Han, Tingting He, Can Feng and Yihan Wang
Sustainability 2024, 16(21), 9368; https://doi.org/10.3390/su16219368 - 28 Oct 2024
Viewed by 1620
Abstract
Artificial intelligence serves as the fundamental catalyst for a new wave of technological innovation and industrial transformation. It holds vital importance in reaching carbon reduction targets and the objectives of “carbon peak and neutrality”. This factor contributes significantly to the reduction in carbon [...] Read more.
Artificial intelligence serves as the fundamental catalyst for a new wave of technological innovation and industrial transformation. It holds vital importance in reaching carbon reduction targets and the objectives of “carbon peak and neutrality”. This factor contributes significantly to the reduction in carbon emissions in the industrial domain. This article utilizes panel data from 30 provinces in China, covering the years 2013 to 2021, to develop an evaluation framework for assessing the progress of artificial intelligence development. Through the use of double fixed-effect models, mediation effect models, and threshold effect models, the empirical analysis examines the industrial carbon reduction effects of artificial intelligence and its operating mechanisms. Research indicates that the advancement of AI can significantly reduce carbon emission intensity within the industrial sector. This conclusion remains valid following comprehensive robustness tests. Furthermore, there exists temporal and regional variability in AI’s impact on industrial carbon reduction, particularly more pronounced after 2016 and in central and western regions. AI influences carbon emission reduction in China’s industrial sector through the advancement and optimization of industrial structures. Here, the increase in senior-level operations acts as a partial masking effect, while optimization serves as a partial mediator. The relationship between AI and industrial carbon emission intensity is non-linear, being influenced by the threshold of government intervention; minimal intervention weakens AI’s effect on carbon intensity reduction. These findings enhance our understanding of the factors influencing industrial carbon emissions and contribute to AI-related research. They also lay a solid empirical groundwork for promoting carbon emission reduction in the industrial domain via AI. Additionally, the results offer valuable insights for formulating policies aimed at the green transformation of industry. Full article
(This article belongs to the Special Issue Carbon Neutrality and Green Development)
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