Research and Application of Carbon Emission Monitoring Technology, Calculation Theory, and Management System

Abstract submission deadline

30 September 2026

Manuscript submission deadline

30 November 2026

Viewed by

3383

Topic Information

Dear Colleagues,

In the context of global efforts to mitigate climate change and pursue carbon neutrality, the transportation infrastructure sector, as a significant contributor to carbon emissions, faces both unprecedented challenges and opportunities. Against this backdrop, carbon emission monitoring, theoretical calculation, and management systems for transportation infrastructure have emerged as critical areas of research, integrating multidisciplinary approaches to achieve low-carbon development throughout the entire lifecycle of infrastructure projects.

Transportation infrastructure, including roads, bridges, railways, airports, and ports, generates substantial carbon emissions across its lifecycle—from material production, design, and construction to operation, maintenance, and eventual demolition. Addressing these emissions requires innovative technologies, rigorous theoretical frameworks, and efficient management systems. This Topic focuses on advancing knowledge and practices in carbon emission monitoring, calculation, and management. This focus covers a wide-range of disciplines, with a relevance that spans multiple journals. Given its clear emphasis on themes pertaining to the goal of sustainability, such as governance/management mechanisms, standardized accounting frameworks, and lifecycle decision-support tools, this topic in alignment with the focus of journals such as Sustainability, Clean Technologies, and Applied Sciences. It is also worth noting that instrument-centered research (such as that utilizing advanced monitoring equipment and data acquisition technology) is of high relevance for journals such as Sensors or CivilEng, while material-centered work (such as that with a focus on low-carbon material development or emission reduction in material production) is of notable relevance to journals such as Materials or Buildings or Energies.

This Topic aims to provide a comprehensive platform for researchers, engineers, and policymakers to publish original research papers and review articles related to carbon emission monitoring, theoretical calculation, and management in relation to transportation infrastructure. We particularly encourage contributions that delve into governance strategies for emission reduction, the development of unified accounting standards, and decision-support models that optimize lifecycle carbon performance, while also welcoming the submission of interdisciplinary work bridging these areas of priority with relevant technological innovations. Such contributions will foster interdisciplinary dialog and collaboration, driving forward the integration of systemic thinking into low-carbon transportation infrastructure practices.

By bringing together diverse perspectives and cutting-edge research on these pressing issues, this Topic seeks to advance the development and implementation of effective solutions for reducing carbon emissions in transportation infrastructure, ultimately contributing to global climate goals. We look forward to receiving your valuable contributions.

Dr. Yang Ding
Dr. Anming She
Dr. Jingliang Dong
Prof. Dr. Shuangxi Zhou
Prof. Dr. Xiaowei Ye
Dr. Youhua Su
Dr. Tao Jin
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Buildings buildings	3.1	4.4	2011	15.1 Days	CHF 2600	Submit
CivilEng civileng	2.0	4.0	2020	21.7 Days	CHF 1400	Submit
Clean Technologies cleantechnol	4.7	8.3	2019	20 Days	CHF 1800	Submit
Energies energies	3.2	7.3	2008	16.8 Days	CHF 2600	Submit
Materials materials	3.2	6.4	2008	15.5 Days	CHF 2600	Submit
Sensors sensors	3.5	8.2	2001	17.8 Days	CHF 2600	Submit
Sustainability sustainability	3.3	7.7	2009	17.9 Days	CHF 2400	Submit

Preprints.org is a multidisciplinary platform offering a preprint service designed to facilitate the early sharing of your research. It supports and empowers your research journey from the very beginning.

MDPI Topics is collaborating with Preprints.org and has established a direct connection between MDPI journals and the platform. Authors are encouraged to take advantage of this opportunity by posting their preprints at Preprints.org prior to publication:

1. Share your research immediately: disseminate your ideas prior to publication and establish priority for your work.
2. Safeguard your intellectual contribution: Protect your ideas with a time-stamped preprint that serves as proof of your research timeline.
3. Boost visibility and impact: Increase the reach and influence of your research by making it accessible to a global audience.
4. Gain early feedback: Receive valuable input and insights from peers before submitting to a journal.
5. Ensure broad indexing: Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (5 papers)

Order results

Content type Publication Date

Result details

Normal Extended Compact

Journals

All Buildings Clean Technol. Energies Sensors CivilEng Materials Sustainability

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

Error

Oops... you haven't selected anything for export.

Ok

clear

24 pages, 1406 KB  

Open AccessArticle

Carbon Footprint (Scope 2) and Energy Intensity per Activity in Intermediate-Complexity Hospitals in the Community of Madrid: Panel Analysis (2016–2024)

by David Campos Vásquez, Mercedes del Río Merino and Paola Villoria Sáez

Buildings 2026, 16(7), 1381; https://doi.org/10.3390/buildings16071381 - 1 Apr 2026

Viewed by 266

Abstract

Hospital buildings account for 60–80% of healthcare sector electricity consumption, yet robust causal evidence on the relationship between building energy efficiency and emissions per unit of clinical activity remains scarce. This study applies within–group fixed effects estimation to an unbalanced panel of 12 [...] Read more.

Hospital buildings account for 60–80% of healthcare sector electricity consumption, yet robust causal evidence on the relationship between building energy efficiency and emissions per unit of clinical activity remains scarce. This study applies within–group fixed effects estimation to an unbalanced panel of 12 intermediate–complexity hospitals in Madrid, Spain (2016–2024; N = 107 hospital–year observations), controlling for activity volume and exogenous shocks. Cluster–robust standard errors and Wild Cluster Bootstrap inference address the limited number of cross–sectional units (N = 12). We propose a methodological correction for the artificial 74.6% discontinuity in Spain’s electricity emission factor (2020–2021) caused by regulatory change. The within–hospital building energy use intensity (EUIe) coefficient is β = 0.646 (p < 0.001), remarkably stable across six robustness specifications (range: 0.599–0.648; 8.2% variation). Wild Cluster Bootstrap confirms statistical significance despite 75% larger standard errors. A 20 kWh/m²·year EUIe reduction achievable through Heating, Ventilation and Air Conditioning (HVAC) retrofit in 1980s–era buildings translates into 13 kWh/stay savings, equivalent to 216 tCO₂/year for median–sized facilities (8.2% reduction). Within R² exceeds 0.97 across all specifications. Building envelope and HVAC retrofit constitute the dominant structural intervention for hospital Scope 2 emissions reduction. Facilities with EUIe > 150 kWh/m²·year should be prioritized for energy efficiency interventions using NextGenerationEU funds. Full article

(This article belongs to the Topic Research and Application of Carbon Emission Monitoring Technology, Calculation Theory, and Management System)

►▼ Show Figures

Figure 1

18 pages, 5743 KB  

Open AccessArticle

Non-Dispersive Gas Analyzer for H₂O and CO₂ Flux Analysis by the Eddy Covariance Method

by Igor Fufurin, Ivan Karpov, Alisa Kosterova, Viacheslav Bessonov, Alexis Yaroslavtsev, Ivan Seregin and Andrey Morozov

Sensors 2026, 26(2), 560; https://doi.org/10.3390/s26020560 - 14 Jan 2026

Viewed by 1147

Abstract

This article presents the stages of development of a non-dispersive infrared (NDIR) open-type gas analyzer prototype (NGAP-1) with a fundamentally new discrete IR radiation generation scheme using pulse-width modulation for measuring the dynamics of water vapor and carbon dioxide concentrations, with further application [...] Read more.

This article presents the stages of development of a non-dispersive infrared (NDIR) open-type gas analyzer prototype (NGAP-1) with a fundamentally new discrete IR radiation generation scheme using pulse-width modulation for measuring the dynamics of water vapor and carbon dioxide concentrations, with further application in the instrument base of an ecological and climatic station to implement the eddy covariance (EC) method. In addition, selecting the component base for NGAP-1, as well as its calibration and experimental field validation as part of an ecological and climatic station, are described. Full article

(This article belongs to the Topic Research and Application of Carbon Emission Monitoring Technology, Calculation Theory, and Management System)

►▼ Show Figures

Figure 1

26 pages, 856 KB  

Open AccessArticle

Exploring Regional Carbon Emission Factors and Peak Prediction: A Case Study of Hubei Province

by Haifeng Xu, Dajun Ren, Yawen Tian, Xiaoqing Zhang, Shuqin Zhang, Yongliang Chen and Xiangyi Gong

Sustainability 2026, 18(1), 329; https://doi.org/10.3390/su18010329 - 29 Dec 2025

Cited by 2 | Viewed by 354

Abstract

Thorough analysis of regional carbon emissions is essential, given their substantial contribution to overall carbon emissions, as it underpins the formulation of targeted low-carbon development strategies. This study investigates whether Hubei Province can achieve carbon peaking by 2030 and explores feasible emission reduction [...] Read more.

Thorough analysis of regional carbon emissions is essential, given their substantial contribution to overall carbon emissions, as it underpins the formulation of targeted low-carbon development strategies. This study investigates whether Hubei Province can achieve carbon peaking by 2030 and explores feasible emission reduction pathways. To address the limitations of existing regional carbon emission studies—particularly the direct use of decomposition factors in prediction models and the lack of logical separation between mechanism analysis and forecasting—a hybrid analytical-predictive framework is proposed. Specifically, the logarithmic mean Divisia index (LMDI) method is first employed to decompose historical carbon emissions and identify the driving forces, while the STIRPAT model combined with the Lasso regression is subsequently used to screen key influencing factors for emission prediction, thereby avoiding the direct use of decomposition factors in forecasting. Based on the selected factors, a genetic algorithm–optimized backpropagation neural network (GA-BP) is developed to predict carbon emissions in Hubei Province from 2024 to 2035. The predictive performance of the GA-BP model is validated using three statistical indicators (R², MAPE, and RMSE) and compared with Extreme Learning Machine (ELM), Support Vector Regression (SVR), and conventional BP models. Furthermore, six development scenarios are designed in accordance with provincial policy objectives to assess the feasibility of carbon peaking. The results indicate the following: (1) Based on the results of the LMDI decomposition, Lasso–STIRPAT analysis, and model sensitivity analysis, per capita GDP is identified as the primary driving factor of carbon emissions in Hubei Province. (2) The GA-BP model demonstrates superior predictive accuracy compared with benchmark models and (3) carbon peaking by 2030 can only be achieved under Scenario 6, highlighting the necessity of coordinated structural and technological interventions. Based on these findings, targeted policy recommendations for carbon emission reduction are proposed. Full article

(This article belongs to the Topic Research and Application of Carbon Emission Monitoring Technology, Calculation Theory, and Management System)

►▼ Show Figures

Figure 1

16 pages, 4027 KB  

Open AccessArticle

Monitoring Method for the Self-Bearing Process During the Construction of Long-Span Steel Roof Structures with Ring Beams

by Wei Lu, Cheng Yuan, Hang Xiong, Yiyang Xiang, Huasheng Xia, Rongying Mao, Jun Teng and Weihua Hu

Buildings 2025, 15(23), 4293; https://doi.org/10.3390/buildings15234293 - 27 Nov 2025

Viewed by 395

Abstract

Long-span steel roofs with spatial ring beams as their primary load-bearing elements are widely adopted in large stadiums and public facilities. Their construction involves complex stages—ring beam assembly, closure and staged falsework unloading—during which boundary conditions, internal forces and overall stiffness change considerably. [...] Read more.

Long-span steel roofs with spatial ring beams as their primary load-bearing elements are widely adopted in large stadiums and public facilities. Their construction involves complex stages—ring beam assembly, closure and staged falsework unloading—during which boundary conditions, internal forces and overall stiffness change considerably. This study proposes a stiffness monitoring method that evaluates the self-bearing process of such structures based on measured stress and displacement data. The method establishes a stiffness matrix for different construction stages and introduces an indicator of stiffness change rate to quantify stiffness evolution during unloading. To validate this approach, finite element method simulations were conducted, and their predictions were compared with monitoring data from the Shenzhen Stadium steel roof. The monitored and simulated stiffness variation trends show strong agreement, thereby validating the applicability of the proposed monitoring framework. The method enables real-time tracking of structural safety, supports optimisation of the unloading sequence and enhances our understanding of stiffness evolution during construction. Overall, this study presents a validated, data-driven framework for monitoring and optimising the self-bearing process of long-span steel roof structures, thereby improving both construction safety and service performance. Full article

(This article belongs to the Topic Research and Application of Carbon Emission Monitoring Technology, Calculation Theory, and Management System)

►▼ Show Figures

Figure 1

19 pages, 6200 KB  

Open AccessArticle

A Macro-Scale Modeling Approach for Capturing Bending-Shear Coupled Dynamic Behavior in High-Rise Structures Using Deep Learning

by Hetian Shao, Wei Lu, Wenchang Zheng, Weihua Hu, Jun Teng and Eric M. Lui

Buildings 2025, 15(20), 3727; https://doi.org/10.3390/buildings15203727 - 16 Oct 2025

Viewed by 612

Abstract

Macro-scale modeling is a fundamental approach for assessing structural damage and occupant comfort in urban high-rises during earthquakes or typhoons. The key to its effectiveness is accurately reproducing dynamic responses and extracting modal characteristics. The critical issue is whether the macro-scale model can [...] Read more.

Macro-scale modeling is a fundamental approach for assessing structural damage and occupant comfort in urban high-rises during earthquakes or typhoons. The key to its effectiveness is accurately reproducing dynamic responses and extracting modal characteristics. The critical issue is whether the macro-scale model can effectively capture Flexure-Shear Coupled (FSC) dynamic behavior. This paper proposes a macro-scale modeling method for high-rise structures with FSC dynamic behavior using deep learning (DL). FSC dynamic behavior is quantified by establishing Displacement Interaction Coefficients (DInC) under each mode shape. To account for the flexural resistance of horizontal members and the anti-overturning contribution of vertical members in high-rise structures, equivalent stiffness parameters representing horizontal and vertical members are introduced into the Lumped Parameter Model (LPM), enhancing the flexibility of the macro-scale model in expressing FSC dynamic behavior. The DInCs are used as input features to identify the LPM’s stiffness parameters, enabling efficient macro-scale modeling. The method was validated on a frame and a frame-core tube structure by comparing dynamic characteristics with their detailed finite element models. This method holds engineering application potential in areas requiring highly accurate and rapid structural characteristic or response calculations, such as seismic response analysis and design optimization of high-rise structures. Full article

(This article belongs to the Topic Research and Application of Carbon Emission Monitoring Technology, Calculation Theory, and Management System)

►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

 

Error

Oops... you haven't selected anything for export.

Ok

clear

Displaying articles 1-5