Low Carbon Objectives Research Articles

Does environmental regulation affect corporate environmental awareness? A quasi-natural experiment based on low-carbon city pilot policy

The low-carbon city pilot (LCCP) policy, as an important measure for China to achieve its "dual carbon" goals, has attracted widespread attention regarding its implementation effects. However, no studies have yet focused on the impact of the LCCP policy on corporate environmental awareness. This study, based on data from Chinese A-share listed companies from 2007 to 2020, employs a triple difference model to evaluate the impact of the LCCP policy on corporate environmental awareness, filling the aforementioned gap in the literature. The results indicate that the LCCP policy can significantly enhance corporate environmental awareness, particularly in non-state-owned enterprises, enterprises in the growth stage, and those located in the eastern regions. The LCCP policy will compel corporations to enhance their environmental awareness by increasing the costs of environmental penalty and environmental governance. However, the analysis of economic consequences reveals that although corporate environmental awareness has improved, it has not directly translated into significant economic benefits or substantial environmental benefits. This may be indicative of a scenario where, in the short term, the cost effect of the LCCP policy outweighs the compensation effect. Nevertheless, the improvement in the quality of information disclosure can significantly mitigate these adverse economic consequences. This research not only enriches the academic outcomes related to environmental regulatory policies but also contributes to ensuring the effective achievement of green, low-carbon objective and the sustainable development of the economy and society.

Evaluation of carbon sink and photovoltaic system carbon reduction along roadside space

As China's photovoltaic (PV) sector experiences rapid growth, the availability of land resources has become a pivotal policy focus, driving the need for comprehensive research and strategic planning for roadside PV initiatives. Utilizing a fuzzy multi-criteria decision-making approach, combined with GIS spatial analysis and a modular design framework, our study quantitatively compared the carbon reduction capabilities of PV systems against the carbon sequestration potential of various vegetative arrangements along the roadside space. The roadside space analysis modular considers a range of factors including topography, meteorology, and construction costs. We examined the spatial distribution of suitability for PV installation and vegetation establishment along the provincial expressway network in China. The results revealed that Inner Mongolia stood out as the frontrunner in carbon reduction potential within high-suitability zones for PV construction, achieving an impressive 4.845 million tons of carbon reduction—nearly four times greater than that of Shaanxi Province. In contrast, the carbon sequestration attributed to vegetation greening in areas less suited for PV development revealed a higher propensity in the southeastern provinces. Guangdong led the charge with an impressive annual carbon sequestration of 2.89 million tons. This was closely followed by Yunnan, Sichuan, Hebei, Guizhou, and Henan, each achieving carbon sequestration amounts exceeding 2 million tons. These results offer valuable quantitative support and practical recommendations for achieving low-carbon objectives in the construction of China's expressways.

Operational Decisions of Construction and Demolition Waste Recycling Supply Chain Members under Altruistic Preferences

How to efficiently and greenly dismantle abandoned buildings and reuse them is a dilemma facing the building material industry’s low-carbon objective. However, relevant studies ignore the influence mechanism of altruistic preferences of enterprises on green dismantling technology in supply chains. Driven by filling this theoretical gap, this paper firstly integrates reciprocal altruism theory and the Stackalberg game method and constructs a construction and demolition waste (CDW) recycling supply chain system consisting of a recycler and a remanufacturer, in which enterprises’ altruistic preferences are considered. The main theoretical outcomes of this paper are as follows. (1) In the case of unilateral altruism, enterprises’ altruistic preference behaviors help in increasing the green dismantling technological level and the amount of CDW recycling. Under the mutual altruism model, the influence of the recycler’s altruistic preference degree on the amount of CDW recycled hinges on the remanufacturer’s altruistic preference degree. (2) The utility of the enterprises and the green dismantling technological level are optimized under the mutual altruism model. (3) In a system of unequal power, unilateral “goodwill” by the follower will have a negative effect on their own interests; the leader plays a crucial role in facilitating equal cooperation and realizing win–win situations. This paper enriches the reciprocal altruism theory in waste management. It also helps in providing guidance for the recycler and remanufacturer in making operational decisions.

Study on Optimization of Land Use Structure in Fujian Province Based on Low-Carbon Perspective

Carbon peaking and carbon neutrality strategies are pivotal in addressing climate change. Optimizing land use structure is a fundamental approach to achieving low-carbon development within a given territory. This study focuses on Fujian Province as the research subject, predicting carbon emissions for the next decade by analyzing the correlation between land use types and carbon emissions using the gray model. This analysis is based on land use panel data spanning from 2007 to 2021. The study applies the FLUS-Markov model to simulate Fujian’s land use in 2030. A multi-objective optimization model is developed from a low-carbon perspective, integrating carbon emissions, economic, and ecological factors. The study explores land use under three scenarios: natural development scenario (NS), low carbon scenario (LCS), and comprehensive scenario (CS). Findings highlight the relationship between land use-related carbon emissions, urbanization, and relevant policies in Fujian. The FLUS-Markov simulations suggest that under the NS scenario, carbon emissions in 2030 will reach 77.829 million tons, an increase of 11.013 million tons from 2020. In contrast, the LCS demonstrates that optimizing land use structures can effectively balance carbon reduction, economic growth, and ecological preservation. Under the CS, 2030 emissions could be reduced by 7.2854 million tons while maintaining economic and ecological benefits. Despite variations in construction land expansion across these scenarios, all follow a “one belt, one core” development pattern. The study concludes with policy recommendations, including industrial layout optimization and clean energy promotion. These findings support the alignment of land use optimization with Fujian’s future development needs, offering guidance for land-use planning and policies focused on low-carbon objectives.

Recombinant strains expressing oxalate decarboxylase efficiently and environmental characteristics of their life cycle assessment

Oxalate decarboxylase has garnered significant attention due to its wide-ranging applications, particularly as an effective demulsifier for oil pollution remediation. However, the accumulation of oxalate decarboxylase by strain is insufficient, and the acquisition of necessary nutrients and energy for biosynthesis also poses challenges to the environment. Therefore, it is crucial to assess the strain's capacity and environmental burden in order to guide future developments and ensure sustainable scale-up. To enhance the protein yield of oxalate decarboxylase from Bacillus mojavensis XH1 (Bacm OxdC), a specialized biodemulsifier, our study investigated the properties of Bacm OxdC and improved its expression efficacy in Escherichia coli from an environmentally friendly perspective. By employing a promoter tandem strategy and optimizing the 5′ untranslated region, recombinant strains C3 and C5 were constructed, resulting in 1.22-fold and 6.90-fold increases in Bacm OxdC expression levels compared to the initial strain C1. The bioprocess of strain C5 achieved a life cycle assessment (LCA)-based environmental impact value of 322.75, representing the lowest environmental impact observed thus far. Improvement strategies included reducing protein purification steps and electricity consumption, which can further mitigate negative environmental impacts and carbon emissions. This study offers a technical strategy for large-scale production of oxalate decarboxylase while attaining green and low-carbon objectives for biological demulsifiers.

Progress in the Research of Features and Characteristics of Mountainous Rural Settlements: Distribution, Issues, and Trends

The study of Features and Characteristics of Mountainous Rural Settlements (RFCMRS) is a key factor in the development of rural settlements during the urbanization process. Mountainous rural settlements, due to their unique mountainous conditions, climate, living environments, and regional culture, are among the important subjects of research for governments and the academic community worldwide. This paper, utilizing the knowledge mapping software CiteSpace (6.2.R3) for co-citation and collaboration analysis, keyword clustering, keyword time zoning, and keyword emergence, analyzes the research trajectory, key issues, and future trends of RFCMRSs. The study finds that current RFCMRS research can be categorized into the following three key issues: “implications of climate change: risks and adaptive responses”, “regional cultural heritage and economic development”, and “ecological conservation and fostering harmonious symbiosis”. Future research will focus on the following three development trends: “risk response based on climate resilience and ecological protection”, “factors of features and characteristics based on regional culture and landscape configurations”, and “human settlements based on low-carbon objectives and sustainable development principles”. Lastly, the paper proposes the following three future research suggestions: “improving the evaluation system for features and characteristics of mountainous rural settlements”, “deepening the study on the evolutionary phenomenon and mechanism for features and characteristics of mountainous rural settlements”, and “exploring the design methods for features and characteristics of mountainous rural settlements based on the concept of sustainable development”.

Artificial intelligence enabled self-powered wireless sensing for smart industry

Traditional batteries or external supply powered wireless sensing system are needed to be improved for realizing the development of the smart industry with low-carbon, green and sustainable. This paper proposes and develops a self-powered wireless sensing system for smart industry (SPOT), utilizing a triboelectric nanogenerator (TENG) coupled with an artificial intelligence (AI) transformer model. The SPOT system includes the TENG-based self-powered flexible sensor (SWNG), the wireless aggregate node (WAN), the electromagnetic and TENG hybrid generator (ETCG), and the monitoring and management center with an AI model (MACA). The ETCG serves as a power source for the WAN. The SWNG acquires voltage signals from products on the conveyor belt in the smart industry, powered by the TENG, and transmits the sensor data wirelessly to the MACA via the WAN for processing. The MACA processes the data using the transformer AI model, which not only ensures self-sustainability and long-term stability but also enables intelligent recognition and monitoring of industrial products by their packaging materials, thereby providing precise status information and decision support for the smart industry. The transformer model’s deployment in the MACA has demonstrated robustness and a high classification success rate of up to 97.8 %, efficiently categorizing multiple targets. Additionally, the SWNG and WAN exhibit low power consumption of approximately 80 mW, successfully contributing to the realization of green, low-carbon objectives. The SPOT system significantly enhances the efficiency of product transportation and management within the smart industry and contributes to the advancement of a sustainable, low-carbon, and green smart industry, offering novel technological insights and pathways for future development.

Clarifying the levers of carbon emission reduction in compact cities in China: A multi-sectoral approach

Compact city policies have gained global attention for their potential to combat urban sprawl, conserve suburban open spaces, reduce energy consumption, and cultivate vibrant urban living environments. While these policies are widely believed to contribute to urban low-carbon objectives, empirical evidence has been lacking. This study focuses on the pivotal elements of compact city development, investigating impacts on sectoral urban carbon emissions and uncovering spatial heterogeneity. The research outcomes affirm that density and accessibility indicators exert significant negative influences on carbon intensity for the energy, industrial, household, and transportation sectors. While public transport, despite its fundamental role in compact city theory, does not show effectiveness in reducing urban carbon emissions as expected. The study highlights that as an approach toward achieving urban sustainability under the scope of climate change adaptation, compact city development strategies necessitate a careful balance in their application. Further, we underscore the importance of complementing compact city policies with greener energy strategies to effectively realize low-carbon urban transportation goals.

Research on Multi-Objective Optimization Design of University Student Center in China Based on Low Energy Consumption and Thermal Comfort

Ensuring optimal building performance is vital for enhancing student activity comfort and fostering energy-saving initiatives toward low-carbon objectives. This paper focuses on university student centers in China, aiming to diminish building energy consumption while enhancing indoor thermal comfort. Parametric modeling of typical cases is executed using the Grasshopper 1.0.0007 software package, and the simulation of building energy consumption and indoor thermal comfort relies on the Ladybug and Honeybee plug-in. Employing a multi-objective optimization design method and the Octopus multi-objective optimization algorithm, this study integrates numerical simulations and on-site surveys to analyze how factors like building form, orientation, envelope structure, and others impact the indoor and outdoor environment. A comprehensive optimization design approach is implemented for the building’s exterior components, including the walls, windows, roof, and shading system. After conducting a comparative analysis of the annual comprehensive energy consumption and indoor thermal comfort before and after the optimization plan, it is determined that implementing these measures reduces the annual comprehensive energy consumption of the building under study by 58.8% and extends the duration of indoor thermal comfort by 53.0%. This study presents a practical optimization design methodology for university student center architecture in China, aiding architects in decision making and advocating for energy-efficient building designs.

Citizen-led decentralised energy futures: Emerging rationales of energy system organisation

The current energy systems are undergoing fundamental changes driven by the climate crisis, unfolding digitalisation and increasing calls for a more active citizens' engagement. The impact of these ongoing trends on the future energy system, however, is far from straightforward. Although there is an increasing shift towards a decentralisation, it is not clear yet how exactly this new decentralised configuration will unfold and materialise. In this article we explore the rationales behind current trends towards a more decentralised electricity system. Given the developments in the electricity system, our study centres on emerging initiatives led by citizens and their values. Theoretically, we first mobilise the notion of socio-technical system as constituted and reproduced by actors, institutions, and technology, operating based on certain shared principles. We use this lens to describe the past and current energy system organisation. Secondly, based on two dimensions of value orientation and steering direction we define four emerging ideal-types socio-technical decentralisation configurations. We examine possible pathways of change, and the institutional, actor and technological preconditions they require, to become predominant forms of decentralisation. We conclude that although all forms of decentralisation may potentially contribute to low carbon objectives, they are likely to co-evolve catering to diverse system needs and the citizen requirements simultaneously.

Carbon Market Efficiency and Economic Policy Uncertainty: Evidence from a TVP-VAR Model

This paper examines the dynamic linkages among economic policy uncertainty (EPU), the green bond market, the carbon market, and the macroeconomy using the time-varying parameter vector autoregressive (TVP-VAR) model with monthly data spanning from January 2016 to December 2021. Additionally, it assesses the robustness and accuracy of the empirical results through the Bayesian vector autoregressive (BVAR) model. The findings indicate that EPU negatively affects the green bond market in the short term but has a positive impact in the medium and long term. Conversely, EPU has a positive impact on the carbon market in the short term but a negative impact in the medium and long term. Furthermore, the green bond market negatively influences the carbon market in both the short and medium to long term. These results suggest that emerging markets, such as the green bond and carbon markets, are influenced by EPU. The adverse impact of the green bond market on the carbon market, however, contributes to expediting China’s attainment of its low-carbon objectives. Appropriate economic policies can play a vital role in accelerating the transition to a low-carbon economy. The study also reveals that the US-China trade war has expedited the development of green capital markets in China, despite its impact on the green economic transition in the country. These findings provide insights for the government and investors to formulate suitable strategies for risk mitigation.

Dynamic Intelligent Scheduling in Low-Carbon Heterogeneous Distributed Flexible Job Shops with Job Insertions and Transfers.

With the rapid development of economic globalization and green manufacturing, traditional flexible job shop scheduling has evolved into the low-carbon heterogeneous distributed flexible job shop scheduling problem (LHDFJSP). Additionally, modern smart manufacturing processes encounter complex and diverse contingencies, necessitating the ability to address dynamic events in real-world production activities. To date, there are limited studies that comprehensively address the intricate factors associated with the LHDFJSP, including workshop heterogeneity, job insertions and transfers, and considerations of low-carbon objectives. This paper establishes a multi-objective mathematical model with the goal of minimizing the total weighted tardiness and total energy consumption. To effectively solve this problem, diverse composite scheduling rules are formulated, alongside the application of a deep reinforcement learning (DRL) framework, i.e., Rainbow deep-Q network (Rainbow DQN), to learn the optimal scheduling strategy at each decision point in a dynamic environment. To verify the effectiveness of the proposed method, this paper extends the standard dataset to adapt to the LHDFJSP. Evaluation results confirm the generalization and robustness of the presented Rainbow DQN-based method.

Multi-agent low-carbon optimal dispatch of regional integrated energy system based on mixed game theory

In the context of global warming and energy scarcity, regional integrated energy system (RIES) can effectively improve energy utilization efficiency and reduce carbon emissions. However, this leads to the increased complexity of the market transactions. To this end, based on the mixed game strategy, a multi-agent low-carbon optimal dispatch model of RIES is proposed in this paper. Firstly, to fully consider the low-carbon objective of the system, a reward and punishment ladder carbon trading mechanism and a ladder integrated demand response (IDR) model are developed. Then, the decision models of the energy management operator (EMO), multiple energy hub agents (EHAs) and the user load aggregator (ULA) are constructed respectively with the goal of economy, and carbon emission flows are introduced as constraints. The interactive trading process of various stakeholders is simulated by a mixed game with master-slave game nested bidding strategy. Finally, the economics and carbon emissions of the system in different scenarios are analyzed by examples, and the effectiveness of the proposed model is verified. Numerical simulation results show that the proposed method not only has good economic and low-carbon environmental protection benefits, but also ensures the interests of various stakeholders.

Carbon storage estimation and strategy optimization under low carbon objectives for urban attached green spaces

In the context of rapid urbanization, scarce land resources have highlighted the importance of attached green spaces (AGS), which have received limited attention despite their critical importance in the urban carbon cycle. Analyzing the differences in carbon storage of different AGS types and proposing different optimization strategies can provide a reference for urban managers to scientifically enhance the carbon sinks of greenfield. This study estimated the carbon storage of AGS in central Shanghai using a series of detailed field survey data and remote sensing data. The results showed that AGS stored 296 Gg (1Gg = 109 g) of carbon, accounting for 56 % of the total carbon storage in all urban green spaces. While carbon density in AGS is lower than that in other green space types due to the dominance of small trees, simple vegetation structures and poor maintenance in some areas. Significant differences in carbon storage capacity were also found among different types of AGS, related to individual vegetation attributes and vegetation structure. Our findings emphasized that optimal design strategies should be different for various green space types, and proposed corresponding optimization strategies for different AGS types. Furthermore, the dual role of multi-layered planting structures in carbon sinks and biodiversity was also emphasized.

Swarm Division-Based Aircraft Velocity Obstacle Optimization Considering Low-Carbon Emissions

In the pursuit of sustainable aviation, this paper presents an innovative approach that adopts a swarm division strategy to enhance and refine the velocity obstacle (VO) method, guided by a low-carbon principle. A dynamic elliptical protection zone model forms the core of this innovative approach. Specifically, this dynamic elliptical protection zone is created based on the difference in aircraft velocity, and a swarm division strategy is introduced in this process. Initially, aircraft that share the same route and type, and have similar velocities and distances, are grouped into swarms. Then, the characteristics of the swarms, such as mass points, velocities, and protection zones, are recorded. Second, the collision cone (CC) between swarms is established, and planar geometrical analysis is used to determine the optimal relief velocity and heading of aircraft on the low-carbon objective while ensuring a safe interval between aircraft in the swarm during the relief period. Additionally, a swarm control algorithm is utilized to adjust the velocity of the aircraft by a small margin. Finally, simulation experiments are conducted using Python, revealing that the swarm relief efficiency of the enhanced VO method sees a notable increase of over 33%. Concurrently, the need for adjustments decreases by an average of 32.78%, while fuel savings reach as high as 70.18%. The strategy is real-time and operational, significantly reduces the air traffic controller (ATC) workload, improves flight efficiency and safety, and contributes positively to the reduction in carbon emissions, which is beneficial for the environment.

Optimal Scheduling Considering Carbon Capture and Demand Response under Uncertain Output Scenarios for Wind Energy

In light of the uncertainties associated with renewable energy sources like wind and photovoltaics, this study aims to progressively increase their proportion in the energy mix. This is achieved by integrating carbon capture devices into traditional thermal power plants and enhancing demand-side management measures, thereby advancing low-carbon objectives in the energy and electricity sectors. Initially, the research proposes utilizing the K-means clustering algorithm to consolidate and forecast the fluctuating outputs of renewable energies such as wind and photovoltaics. Further, it entails a comprehensive analysis of low-carbon resources on both the supply and demand sides of the electricity system. This includes installing carbon storage and power-to-gas facilities in carbon capture plants to create a versatile operating model that can be synchronized with wind power systems. Additionally, the limitations of carbon capture plants are addressed by mobilizing demand-side response resources and enhancing the system’s low-carbon performance through the coordinated optimization of supply and demand resources. Ultimately, this study develops an integrated energy system model for low-carbon optimal operation, aimed at minimizing equipment investment, carbon emission costs, and operational and maintenance expenses. This model focuses on optimizing the load and supply distribution plans of the electrical system and addressing issues of load shedding and the curtailment of wind and solar power. Validation through three typical scenarios demonstrates that the proposed scheduling method effectively utilizes adjustable resources in the power system to achieve the goal of low-carbon economic dispatch.

Enhancing operation flexibility of distributed energy systems: A flexible multi-objective optimization planning method considering long-term and temporary objectives

Flexibility is a crucial capability that distributed energy systems need to possess. Flexible distributed energy systems can attain the objectives of daily economic operation for users, temporary demand response for the power grid, and low-carbon operation for environmental benefits. Among these objectives, economy and low-carbon objectives are long-term objectives that need to be met routinely, while grid-friendly interactions only occur temporarily but need to be met as a priority. Current multi-objective optimization planning models disregard the inherent differences in temporal property of these objectives, which may weaken the system's flexibility. Here, a novel multi-objective optimization model is proposed to enhance the flexibility of distributed energy systems. First, a new flexibility index is formulated, in which diverse objectives are evaluated synergistically across different scenarios. Second, a flexible mixed-integer multi-objective programming model is developed, which returns the most flexible system design and its operation strategies for each objective. The application in a real case in Tianjin, China revealed that, compared with the conventional multi-objective planning method, the proposed method could improve the system flexibility by 6%–10 %. The universality of the proposed method was further verified by traversal analysis.

Research on crystalline admixtures for low carbon buildings based on the self-healing properties of concrete

The increasing fascination surrounding low carbon construction materials has propelled self-healing concrete into the realm of vital technologies for attaining low carbon objectives. This investigation through artificial cracking and precompression experiments demonstrates the efficacy of two specific crystalline admixtures (CAs), namely L-aspartic acid (LAA) and Ethylenediaminetetraacetic acid (EDTA), in augmenting the self-healing characteristics of concrete. The flexural and compressive strength as well as chloride ion diffusion coefficient tests, were used to evaluate the mechanical properties and durability, and the microstructures was analyzed by X-ray diffraction (XRD), differential thermogravimetric analysis (TG-DTG), scanning electron microscopy (SEM), Bruno-Emmett-Teller (BET) and mercury intrusion pore size measurement (MIP). The findings indicate that the optimum dosage for both LAA and EDTA is determined to be 0.5%. At this dosage level, these admixtures effectively fill the pores by stimulating the production of hydrated calcium silicate gel (CSH) and calcium carbonate, leading to a reduction in porosity, enhanced concrete compactness, and successful activation of the self-healing mechanism. Moreover, the incorporation of these admixtures results in improved strength grade and enhanced resistance against chloride ion penetration.

Dynamic simulation of carbon emission under different policy scenarios in Pearl River Delta urban agglomeration, China.

Global climate continues to warm; by reducing carbon emission (CE) to cope with climate warming has become a global consensus. The influencing factors of CE exhibit diversification and spatial characteristics, and the complexity of the CE system poses challenges to green and low-carbon development and the realization of China's dual-carbon goals. Taking the Pearl River Delta urban agglomeration as an example, this study explored the influencing factors of CE and designed emission reduction schemes with the help of multi-scale geographically weighted regression (MGWR). Based on this, the system dynamics model was used to construct a CE system framework considering multi-dimensional driving factors, so as to combine the complex CE system with the emission reduction countermeasures considering spatial heterogeneity, and realize the dynamic simulation of CE reduction policies. The results showed that the urban agglomeration as a whole will reach carbon peak by 2025. Shenzhen, Zhuhai, and Dongguan have achieved carbon peak before 2020, while other cities will reach carbon peak by 2025-2030. The government policy constraints can effectively curb CE, but if government constraints were relaxed, CE will rise and individual cities will not reach carbon peak. Comprehensive CE reduction policies are better than a single CE reduction policy. The study found that this model framework provides a systematic analysis of carbon reduction strategies for urban agglomerations, offering decision-makers various combinations of economic development and green low-carbon objectives. This will further contribute to a multi-faceted mitigation of high emission in urban agglomeration and promote regional sustainable development.

Research on the Correlations between Spatial Morphological Indices and Carbon Emission during the Operational Stage of Built Environments for Old Communities in Cold Regions

The escalation of the urban population and energy demands has exacerbated the carbon emission intensity at the operational stage of urban old communities. The spatial elements of the built environments comprising building groups, roads and landscape, and the spatial morphology of these elements, are endowed not only with human activities but also impact local microclimates and overall carbon emissions. Nonetheless, little attention has been paid to the correlation mechanism between the spatial morphology of the urban built environments and carbon emissions. In this paper, the aim is to combine carbon emissions simulation and statistical analysis to find the correlation between the spatial morphological indices and carbon emissions and to bridge the gaps. Thus, guided by the principles of urban energy modeling, this research adopts a parametric process of “information model construction–carbon emission simulation–statistical analysis”. First, taking 60 typical samples of an old community in Jinan, China, as objects, morphological indices such as density, texture and layout are analyzed through regression analysis to highlight their impacts on carbon emissions. Then, a carbon emission prediction model based on spatial morphological indices is established and verified. The results show that the floor area ratio (FAR), building coverage ratio (BCR), enclosure degree (ED), shape factor (SF) and average road aspect ratio (AS) have significant impacts on carbon emissions during the operational stage. Among these indices, the FAR and the ED are identified as the pivotal influencers. The findings confirm the important role of spatial morphological design of old communities in cold regions in improving urban carbon reduction potential, and they provide theoretical underpinnings and empirical data as references for urban morphology design formulated within the context of low-carbon objectives.