Energy Carbon Emissions Research Articles

Life cycle carbon emission assessment and carbon payback period analysis for the regeneration of old residential areas in cold regions: Case study in Qingdao, China

Communities urgently need to explore green and low-carbon transformation pathways due to high energy consumption and carbon emissions. However, existing studies lack a comprehensive assessment of carbon emissions and analysis regarding carbon reduction during renovation of old residential areas in cold regions. Therefore, this study employs a Life Cycle Assessment (LCA) and the payback period method to evaluate carbon emissions throughout the life cycle comprehensively. It constructs a carbon-emission calculation model based on LCA, specifically tailored to the retrofitting of old residential areas in cold cities. Additionally, it investigates the effectiveness of three typical retrofit measures undertaken in Qingdao, a region located in the cold A zone, focusing on whole-life-cycle energy savings and carbon reduction. The findings reveal that the average annual carbon emission intensity per unit area for Cases 1, 2, and 3 is 12.59 kg-CO2 e/(m2·a), 27.05 kg-CO2 e/(m2·a), and 23.39 kg-CO2 e/(m2·a), respectively. Their corresponding carbon payback periods were 6.06, 7.23, and 16.00 years, respectively, which could be further shortened through material recycling. The environmental impact assessment of typical retrofitting measures contributes to the promotion of sustainable development. Furthermore, this study offers guidance for establishing an effective assessment system that supports the design of energy-saving retrofits.

Renewable energy-driven for sustainable off-grid desalination: A comprehensive review on technical highlights and process

Despite the traditional desalination processes have achieved tremendous progress in terms of technology and cost, it is still limited by the high energy consumption and carbon emissions brought about by expensive fossil fuels. Finding alternative energy sources is essential to meet the growing demand for desalination. Over the past few decades, plenty of efforts have focused on driving the desalination process through renewable energy sources in order to achieve the goal of environmentally friendly off-grid desalination and zero liquid discharge. However, limited by technology and economic cost, the desalination processes driven by renewable energy are difficult to carry out large-scale application, and they are more used as an auxiliary unit of the traditional desalination processes. First of all, this article reviews the basic principles of solar interface evaporation technology, and further, summarizes the sustainable desalination technologies driven by solar and geothermal energy completely. In the end, the current global environment and energy troubles are discussed, and the necessity and significance of promoting renewable energy for desalination in the future are emphasized. This review will assist to promote attention to the development and utilization of renewable energy sources, with a focus on large-scale renewable-driven off-grid desalination projects in the future.

Spatiotemporal Evolution and Tapio Decoupling Analysis of Energy-Related Carbon Emissions Using Nighttime Light Data: A Quantitative Case Study at the City Scale in Northeast China

As the world’s second-largest economy, China has experienced rapid industrialization and urbanization, resulting in high energy consumption and significant carbon emissions. This development has intensified conflicts between human-land relations and environmental conservation, contributing to global warming and urban air pollution, both of which pose serious health risks. This study uses nighttime light (NTL) data from 2005 to 2019, along with scaling techniques and statistical analysis, to estimate city-scale energy carbon emissions over a 15-year period. Focusing on Northeast China, a traditional industrial region comprising 36 cities across three provinces, we examine spatial patterns of energy carbon emissions and assess spatiotemporal evolution through spatial autocorrelation and dynamic changes. These changes are further evaluated using standard deviation ellipse (SDE) parameters and SLOPE values. Additionally, the Tapio decoupling index is applied to explore the relationship between city-scale emissions and economic growth. Our findings for the 36 cities over 15 years are: (1) Heilongjiang shows low, declining emissions; Jilin improves; Liaoning has high, steadily increasing emissions. (2) The global spatial autocorrelation of energy carbon emissions is significant, with a positive Moran’s I, while significant local Moran’s I clusters are concentrated in Heilongjiang and Liaoning. (3) The greatest emission changes occurred in 2015, followed by 2019, 2005, and 2010. (4) Emission growth is fastest in Heilongjiang, followed by Liaoning and Jilin. (5) Tapio analysis shows positive decoupling in Heilongjiang, declining decoupling in Jilin, and no change in Liaoning. This study provides a quantitative basis for dual carbon goals and offers emission reduction strategies for government, industry, and residents, supporting energy transition and sustainable urban planning.

Particle-Solid Transition Architecture for Efficient Passive Building Cooling.

Electricity consumption for building cooling accounts for a significant portion of global energy usage and carbon emissions. To address this challenge, passive daytime radiative cooling (PDRC) has emerged as a promising technique for cooling buildings without electricity input. However, existing radiative coolers face material mismatch issues, particularly on cementitious composites like concrete, limiting their practical application. Here, we propose a cementitious radiative cooling armor based on a particle-solid transition architecture (PSTA) to overcome these challenges. The PSTA design features an asymmetric yet monolithic morphology and an all-inorganic nature, decoupling radiative cooling from building compatibility while ensuring UV resistance. In the PSTA design, nanoparticles on the surface serve as sunlight scatterers and thermal emitters, while those embedded within a cementitious substrate provide build compatibility and cohesiveness. This configuration results in enhanced interfacial bonding strength, high solar reflectance, and strong mid-infrared emittance. Specifically, the PSTA delivers an enhanced interfacial shear strength (0.93 MPa), several-fold higher than that in control groups (metal, glass, plastic) along with a cooling performance (a subambient temperature drop of ∼6.6 °C and a cooling power of ∼92.8 W under a direct solar irradiance of ∼680 W/m2) that rivals or outperforms previous reports. Importantly, the design concept of the PSTA is applicable to various particles and solids, facilitating the practical application of PDRC technology in building scenarios.

Triboelectric Nanogenerators Based on Transition Metal Carbo-Chalcogenide (Nb2S2C and Ta2S2C) for Energy Harvesting and Self-Powered Sensing.

With burgeoning considerations over energy issues and carbon emissions, energy harvesting devices such as triboelectric nanogenerators (TENGs) are developed to provide renewable and sustainable power. Enhancing electric output and other properties of TENGs during operation is the focus of research. Herein, two species (Nb2S2C and Ta2S2C) of a new family of 2D materials, Transition Metal Carbo-Chalcogenides (TMCCs), are first employed to develop TENGs with doping into Polydimethylsiloxane (PDMS). Compared with control samples, these two TMCC-based TENGs exhibit higher electric properties owing to the enhanced permittivity of PDMS composite, and the best performance is achieved at a concentration of 3wt. ‰ with open circuit voltage (Voc) of 112V, short circuit current (Isc) of 8.6µA and charge transfer (Qsc) of 175nC for Nb2S2C based TENG, and Voc of 127V, Isc of 9.6µA, and Qsc of 230nC for Ta2S2C based TENGs. These two TENGs show a maximum power density of 1360 and 911mWm-2 respectively. Moreover, the tribology performance is also evaluated with the same materials, revealing that the Ta2S2C/PDMS composite as the electronegative material presented a lower coefficient of friction (COF) than the Nb2S2C/PDMS composite. Their applications for energy harvesting and self-powered sensing are also demonstrated.

Facilitating Charge Separation of Donor–Acceptor Conjugated Microporous Polymers via Position Isomerism Strategy for Efficient CO2 Photoconversion

Photoreduction of CO2 into the chemical feedstocks of fuels provides a green way to help solve both the energy crisis and carbon emission issues. Nevertheless, undesirable charge separation and migration, as well as rapid reverse charge recombination results in the unsatisfactory photocatalytic activity of most conjugated microporous polymers (CMPs). Herein, a pair of donor–acceptor (D-A) CMPs for CO2 photoconversion was developed through position isomerism by altering linkage sites. The results show that Py-D27F with 2,7-site linkage exhibits a completely conjugated structure and a large molecular dipole moment, thus significantly accelerating charge separation and transfer. As a result, Py-D27F achieves a higher yield of 320.9 μmol g−1 h−1 for CO2-to-CO photoreduction (without the addition of any photosensitizers and precious metals), which is about three-fold greater than that of Py-D36F (3,6-site linkage). This study provides a valuable idea for exploring outstanding CMP photocatalysts for the efficient processing of photocatalysis.

Compressive Strength Characteristics of Compressed Stabilized Earth Block (CSEB) Units and Walls

This paper is mainly focused on compressive strength behavior of Compressed Stabilized Earth Block (CSEB) units and CSEB walls. Compressed Stabilized Earth Block (CSEB) is a rectangular block used in wall construction. The ingredients of CSEB are soil, cement, fine aggregate, crusher dust, and a small amount of water. These blocks have less energy consumption and carbon emission, and they provide improved thermal insulation. In addition, they use local resources and disseminate appealing aesthetics with elegant profile and uniform size. Due to these advantages CSEB can be used as a green construction material. This research aims to study the strength characteristics of CSEB wall in compression and evaluate the suitability of CSEB walls as load bearing walls in structures. This research studies physical and mechanical characteristics of CSEB units made from red residual soil of Lele (Lalitpur) with 8% cement for stabilization. This paper discusses the compressive strength behavior of walls constructed of size 0.660m x 1.100m x 0.220m using CSEB units in cement sand mortar and stabilized mud mortar separately which were tested after 28 days. The experimental values after laboratory testing of CSEB masonry wall with height to thickness ratio 5:1 for cement sand mortar (1:6) and stabilized mud mortar (stabilized with 8% cement and 16% extra sand) separately are compared with relevant values from different codes. Results obtained from compressive strength tests of masonry walls constructed in the laboratory and those values from different codes concerning the strength of masonry unit and mortar are compared and found to be in agreement. The comparison of laboratory results with codal provisions of design of masonry walls illustrate that CSEB masonry walls can be designed in the similar way as brick masonry walls.

Life cycle carbon emission calculation model of energy system in public buildings: A case study in Shanghai

Energy consumption and carbon emissions (CE) in the building sector have grown in importance in relation to global climate change for China's sustainable development. Relatively few research have addressed the embodied CE, despite the fact that many have examined the operational CE from energy system operational use. In order to determine the CE of the energy production, transportation, construction, operation, and recycling stages, we therefore put up an integrated model. An inventory analysis of the energy system's life cycle CE, a scenario forecast of various combinations of building energy system equipment, and a case study of the building energy system of a typical office building in Shanghai's service industry. According to the results, the embodied CE emits 38.01 % of its energy, and the operational CE emits 61.99 %. However, we should not underestimate the significance of the impact of the embodied CE. Among all projection scenarios, the suggested CE share is highest (18.40 %) when the system combination of air-source heat pump units, LED lights, and ground-source heat pump units is implemented. In the end, based on the structural analysis of the embodied CE, low-carbon growth strategies and policy recommendations for Shanghai's service sector will be made.

Coordinated scheduling optimization of building integrated energy system with flexible load

Under the background of China's "dual carbon" goals of peaking carbon emissions by 2030 and achieving carbon neutrality by 2060, this paper proposes a method for the optimization of source-load coordinated scheduling in Building Integrated Energy System (BIES) with Flexible Load (FL) to promote sustainable and efficient energy development and enhance energy utilization efficiency. The method starts by analyzing the energy consumption behavior characteristics on the load side of buildings, determining the potential for FL reduction based on national standards and human comfort requirements. It then applies price-based demand response measures to shiftable and transferrable loads and incentives-based demand response measures to FL identified as reducible through potential analysis. Subsequently, a multi-objective optimization model focusing on economic efficiency and carbon emissions is established to coordinate the response curves of FL on the demand side and the operation of energy equipment on the supply side. Finally, the coordination optimization model is solved using a synergistic approach combining the logistic chaos mapping and adaptive t-distribution enhanced Non-dominated Sorting Dung Beetle Optimizer (NSDBO) algorithm, ensuring superior search effectiveness. Simulation results demonstrate that the optimization of FL responses using the Logistic-t-NSDBO algorithm effectively reduces peak-load variations while significantly lowering energy consumption costs and carbon emissions on the energy supply side.

Comparison and regional suitability evaluation of different backfill source heat pumps using TRNSYS

As global energy demand and carbon emissions continues to rise year by year, the use of renewable energy for heating becomes increasingly urgent. This study employs solar and geothermal energy to supply heat to mining facilities. Initially, three distinct heating systems for mining areas were modeled in TRNSYS: the Backfill Source Heat Pump (BSHP), the Solar Assisted Backfill Source Heat Pump (SABSHP), and the Solar Assisted Backfill Source Heat Pump with Seasonal Heat Storage (SABSHP-SHS). Following a decade of simulation, the optimal system was identified through a comparative analysis that encompassed backfill temperature equilibrium, the inlet and outlet water temperatures for buried pipes, Coefficient of Performance (COP), Partial Load Ratio (PLR), and economic factors. Over the 10-year simulation period, the COP and PLR of the SABSHP-SHS system outperformed both the BSHP and SABSHP systems, showing higher energy efficiency and better heat pump performance. To further assess the regional adaptability of the SABSHP-SHS system, simulations were conducted across four regions with diverse climatic profiles: Shijiazhuang, Yan’an, Xining, and Hegang. The analysis focused on heat collection, efficiency, backfill temperature variation, system energy consumption, and the energy efficiency ratio. The findings indicate that Hegang exhibits the highest solar heat collection efficiency at 89.9% during the non-heating season, while Xining achieves a significantly higher heat collection efficiency of 54.1% during the heating season. These results suggest that the SABSHP-SHS system is particularly effective in regions with pronounced seasonal variations, characterized by extended cold winters and brief, intense summers.

Driving low-carbon mechanisms through smart investments in renewable resources and green finance initiatives among G20 nations

In light of the escalating concerns regarding climate change and environmental decline, major nations are actively exploring strategies to mitigate environmental harm and achieve future sustainability. The surge in economic expansion in developed economies is linked to an increase in CO2 emissions. Consequently, in their pursuit of carbon-neutral policies, these countries are increasingly turning towards renewable energy as a means to enhance resource conservation and efficiency. This research investigates the varied impacts of renewable energy investment, green finance, geopolitical risk, GDP growth, foreign direct investment, and gross fixed capital formation on the carbon emissions of G20 countries. The study uses the CUP-FM (Continuously Updated Fully Modified) and CUP-BC (Continuously Updated Bias-Corrected) estimators, which are sophisticated econometric approaches designed to handle non-stationary panel data and cross-sectional dependency, to produce robust long-term parameter estimates. The CUP-FM estimator adjusts for potential endogeneity and serial correlation, improving the accuracy of long-run relationships in panel data. The CUP-BC estimator provides bias-corrected estimates to further enhance the precision of these long-run connections.The long-term parameter estimates reveal a negative correlation between renewable energy investment, green finance, and carbon emissions. In contrast, foreign direct investment, gross fixed capital formation, GDP growth, and geopolitical risk are positively associated with CO2 emissions. This suggests that financial stability often leads to investments in carbon-heavy economic ventures, thereby implicating economic growth as a contributing factor to environmental degradation in G20 countries.

Grouping-maintenance of multi-components production system under ecological background

Purpose: The purpose of this paper is to propose an opportunistic group maintenance model for a multi-component series system considering reducing CO2 emission and increasing system efficiency, which establishes a maintenance policy to minimize the cost rate of the system life cycle.Design/methodology/approach: Structural dependence and economic dependence between the components in a multi-component series system are analyzed to make a condition-based maintenance policy. To minimize the cost rate of the system life cycle, clustering theory and two decision variables, which include the preventative maintenance cycle multiplier and basic preventive maintenance interval of each component, is utilized to make an opportunistic policy for system optimal maintenance under the background of increasing energy efficiency and reducing CO2 emissions.Findings: It can be concluded that the government imposes fines on excessive CO2 emissions and energy consumption indicators, which can influence the maintenance decisions of enterprises, promote enterprises to shorten the cycle of preventive maintenance, and avoid excessive CO2 emissions of various components and exceed the indicators as much as possible, so as to enable enterprises to actively save energy, reduce emissions and control carbon emissions. Furthermore, when the single preparation cost of repair maintenance increases, enterprises need to shorten the maintenance period to avoid frequent component failures. As the cost of a single prep for preventive maintenance rises, organizations need to extend maintenance cycles and avoid frequent parts downtime - minimizing their own repair costs.Practical implications: Considering the high maintenance cost and low energy efficiency of multi-component systems, this model assists production managers to have better maintenance of these systems.Originality/value: 1.Model innovation: comprehensive consideration of two variables in the selection of decision variables for preventive maintenance or opportunistic maintenance; The selection and synthesis of ecological factors when making ecologically conscious maintenance decisions for multi-component systems make up for the gaps of single variables and one-sided indicators in previous studies and models. 2. Methodological innovation: In the identification of opportunity maintenance, the idea of clustering is first combined; In the simulation analysis, genetic algorithm is used to obtain the optimal parameters quickly and accurately. A blend of management, statistics, biology and computer science.

Energy self-sufficiency and carbon neutrality potential of Chinese urban wastewater treatment

The implementation of some energy-saving and emission reduction measures can effectively offset the energy consumption and carbon emissions in the wastewater treatment process. Integrated policy-making information on carbon-energy-economy of these technologies is not clear in national wastewater treatment industry. This study investigated energy self-sufficiency, carbon neutralization potential and economic performance of wastewater treatment industry, as well as related synergy effect among the three aspects using the proposed approach. Then the approach was used to investigate energy-saving and carbon emission reduction potential of the three technologies (photovoltaic power (PV), combined heat and power (CHP) and water source heat pump (WSHP)) and technological combinations as well as related economic performance in urban wastewater treatment of Chinese different provinces in 2021. The results showed that (1) The national energy self-sufficiency is 95%; therein, 13 provinces have the potential to achieve or exceed energy self-sufficiency of 100%, and the top 3 provinces are Heilongjiang, Yunnan and Hunan. (2) The national carbon neutral potential is 87%; therein, 9 provinces have the potential to achieve or surpass carbon neutralization of 100%, and the top 3 provinces include Beijing, Hunan and Shaanxi. (3) these technologies or their combinations are potentially economically viable (net economic benefit ratio >1), and the top three provinces are Beijing, Shaanxi, and Inner Mongolia. (4) Generally, there are 13 provinces with synergy effect >1 in the country; the top three provinces are Liaoning, Heilongjiang and Hunan. Finally, targeted policy suggestions are provided for Chinese urban wastewater treatment. The proposed approach can also act as one of decision-making tools for wastewater treatment industry in other countries or areas in the world.

Efficient Pump Scheduling for Large‐Scale Multiproduct Pipelines Using Deep Reinforcement Learning

ABSTRACTOptimizing pump scheduling in multiproduct pipelines can significantly reduce energy consumption and carbon emissions. For pump scheduling in multiproduct pipelines, to describe hydraulic losses more accurately, the model needs to adopt shorter discrete time intervals, which will lead to longer decision‐making time. It combined with the large solution space of large‐scale pipelines, will lead to low solution efficiency with dynamic programming methods and poor solution quality with heuristic optimization algorithms. Given that, this article develops a multiproduct refined oil transmission simulation system and employs the enhanced Proximal Policy Optimization (PPO) algorithm with action space shaping trick to optimize pump scheduling for large‐scale multiproduct pipelines. The method of converting discrete action space to multi‐discrete action space through action shaping can address PPO's low convergence efficiency issue resulting from the large discrete action space challenge common in large‐scale multiproduct pipelines. The experimental results indicate that the proposed method, that is, PPO algorithm with multidiscrete action space exhibits significant advantages in terms of efficiency and robustness in large‐scale pipelines compared to mainstream methods for pump scheduling such as dynamic programming (DP), genetic algorithms (GA), and ant colony optimization (ACO). Furthermore, we demonstrate the effectiveness of action space shaping in large‐scale pipelines from the perspectives of exploration and exploitation.

Evolutionary Game Analysis of Collaborative Prefabricated Buildings Development Behavior in China under Carbon Emissions Trading Schemes

Prefabricated buildings (PBs) are considered a green way to reduce energy consumption and carbon emissions in the construction industry due to their environmental and social benefits. However, PBs have obstacles such as high construction costs, immature technology, and insufficient policy incentives, and developers’ willingness to develop them needs to be higher. Therefore, it is necessary to explore how to motivate more developers to develop PBs. In this paper, we first discuss the impact of the carbon emissions trading scheme (ETS) on the construction industry and then consider the heterogeneity of construction developers, introduce a collaborative mechanism to establish a three-party evolutionary game model between the government and the heterogeneous developers, and explore the evolution of the three-party dynamic strategies through numerical simulation. The results show that developers’ initial development probability affects the system’s evolutionary trend, and the developer who obtains more low-carbon benefits plays a dominant role. Further analyses show that critical factors such as market profitability, synergistic benefits, and carbon tax price positively influence the development of PBs, and the influence of synergistic cooperation mechanisms should be especially emphasized. This study provides practical insights into the sustainable development of the construction industry and the government’s development of a suitable carbon portfolio policy for it. Including the construction industry in the ETS is recommended when carbon prices reach 110 RMB/t. At this point, the government can remove the subsidy for PBs, but the behaviors of the developers who participate in the ETS still need to be supervised.

A review of resistance–capacitance thermal network model in urban building energy simulations

With the increasing population and urbanization promotion, energy consumption and carbon emissions have increased, and concern for inefficient energy use and deteriorating urban environments is growing. The prediction of building energy consumption and environmental evaluation, especially at large scales, are considered to be a major challenge confronting the research community. An outstanding strategy for mitigating energy consumption and carbon emissions resides in the field of energy modeling. As a simplified building energy modeling model, resistance–capacitance (RC) network model has the applications in fast predicting building energy consumption. Notably, in recent years, there has been an evident absence of thorough review endeavors related to RC model for urban building energy loads and climate. This review systematically explores the application of low-order models and reduction methods for urban or regional energy simulation starting from the typical RC model for building. In summation, the challenges associated with employing this model for urban building energy loads and climate can be succinctly summarized as follows: the absence of a unified platform for RC modeling; insufficient readily applicable urban datasets; the need for modeling tools that facilitate cross-platform analyses in conjunction with existing Geographic Information Systems (GIS) and urban thermal environment simulation research platforms, and the essential requirement for seamless integration with other complementary modules.

A succinct state-of-the-art survey on green cloud computing: Challenges, strategies, and future directions

Cloud computing is a method of providing various computing services, including software, hardware, databases, data storage, and infrastructure, to the public through the Internet. The rapid expansion of cloud computing services has raised significant concerns over their environmental impact. Cloud computing services should be designed in a green manner, efficient in energy consumption, virtualized, consolidated, and eco-friendly. Green Cloud Computing (GCC) is a significant field of study that focuses on minimizing the environmental impact and energy usage of cloud infrastructures. This survey provides a comprehensive overview of the current state of GCC, focusing on the challenges, strategies, and future directions. The review study begins by identifying important challenges in GCC from practical implementations, identifying GCC-introduced environmental protection and prevention initiatives, and expressing the demand for long-term technical progression. It then addresses GCC’s primary concerns, such as energy efficiency, resource management, operational costs, and carbon emissions, and categorizes implementations according to algorithms, architectures, frameworks, general issues, and models and methodologies. Furthermore, enhancements in virtualization, multi-tenancy, and consolidation have been identified, analyzed, and accurately portrayed to address the advancements in GCC. Finally, the survey outlines future research directions and opportunities for advancing the field of GCC, including the development of novel algorithms, technologies for energy harvesting, and energy-efficient and eco-friendly solutions. By providing a comprehensive overview of GCC, this survey aims to serve as documentation for further evolving new emerging technological approaches in the GCC environment.

Low-carbon Development Path of Ordos Based on LEAP Model

To determine the low-carbon development path of Ordos, three scenarios （baseline scenario, low carbon scenario, and enhanced low carbon scenario） were constructed based on the LEAP model to forecast the energy demand and carbon emission in Ordos from 2020 to 2050 and to analyze the contribution of various policy initiatives to reduce carbon emission. The results showed that under the enhanced low carbon scenario, the energy demand in Ordos peaked at 52 million tons of standard coal equivalent in 2025 and decreased to 40 million tons of standard coal equivalent in 2050, and carbon emissions peaked at 163 million tons in 2025 and decreased to 16 million tons in 2050, which was 88% lower than that in 2020. Regarding emission reduction contribution, comparing the baseline scenario and the enhanced low-carbon scenario, the increase in renewable energy power generation installation, the reduction in energy consumption of terminal energy use, and the increase in terminal electrification rate contributed to the emission reductions of 43%, 25%, and 24%, respectively. The Ordos should vigorously develop renewable energy and make full use of the rich endowment of wind and light resources； at the same time, it should promote economic transformation and gradually increase the proportion of high-value-added and low-energy-consuming industries in the industrial structure. For the power sector, the power generation structure should be adjusted. Traditional thermal power generation should be replaced by zero-carbon and low-carbon power generation technologies. For the industrial and transportation sectors, the terminal electrification rate should be increased, and the energy intensity should be reduced.

The Application and Evaluation of the LMDI Method in Building Carbon Emissions Analysis: A Comprehensive Review

The Logarithmic Mean Divisia Index (LMDI) method is widely applied in research on carbon emissions, urban energy consumption, and the building sector, and is useful for theoretical research and evaluation. The approach is especially beneficial for combating climate change and encouraging energy transitions. During the method’s development, there are opportunities to develop advanced formulas to improve the accuracy of studies, as indicated by past research, that have yet to be fully explored through experimentation. This study reviews previous research on the LMDI method in the context of building carbon emissions, offering a comprehensive overview of its application. It summarizes the technical foundations, applications, and evaluations of the LMDI method and analyzes the major research trends and common calculation methods used in the past 25 years in the LMDI-related field. Moreover, it reviews the use of the LMDI in the building sector, urban energy, and carbon emissions and discusses other methods, such as the Generalized Divisia Index Method (GDIM), Decision Making Trial and Evaluation Laboratory (DEMATEL), and Interpretive Structural Modeling (ISM) techniques. This study explores and compares the advantages and disadvantages of these methods and their use in the building sector to the LMDI. Finally, this paper concludes by highlighting future possibilities of the LMDI, suggesting how the LMDI can be integrated with other models for more comprehensive analysis. However, in current research, there is still a lack of an extensive study of the driving factors in low-carbon city development. The previous related studies often focused on single factors or specific domains without an interdisciplinary understanding of the interactions between factors. Moreover, traditional decomposition methods, such as the LMDI, face challenges in handling large-scale data and highly depend on data quality. Together with the estimation of kernel density and spatial correlation analysis, the enhanced LMDI method overcomes these drawbacks by offering a more comprehensive review of the drivers of energy usage and carbon emissions. Integrating machine learning and big data technologies can enhance data-processing capabilities and analytical accuracy, offering scientific policy recommendations and practical tools for low-carbon city development. Through particular case studies, this paper indicates the effectiveness of these approaches and proposes measures that include optimizing building design, enhancing energy efficiency, and refining energy-management procedures. These efforts aim to promote smart cities and achieve sustainable development goals.

Carbon Emission Analysis of RC Core Wall-Steel Frame Structures

The development of super high-rise building projects has become crucial for mitigating land shortages in rapidly growing urban areas. Super high-rise steel structures, particularly RC core wall-steel frame systems, have become the preferred choice due to their superior performance, high prefabrication level, and construction efficiency. Despite their benefits, super high-rise buildings face challenges related to higher energy consumption and carbon emissions. Consequently, it is important to analyze the carbon emissions of these buildings throughout their lifecycle and propose low-carbon construction strategies. A carbon emission analysis focused on super high-rise buildings with RC core wall-steel frame structures is conducted in this study. A carbon emission analysis model is constructed based on BIM-enabled LCA through a real-world case study. The emission factor method is combined with the BIM model to calculate carbon emission. Furthermore, carbon emissions across various construction strategies are compared, with a particular focus on the manufacturing processes of the main materials. The results indicate that incorporating admixtures in concrete, along with adopting the electric arc furnace (EAF) method and utilizing recycled scrap steel in steel manufacturing, significantly reduces the carbon emissions of the buildings. Lastly, effective low-carbon approaches for these buildings are proposed.