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Global climate change, driven by increasing carbon emissions, has posed a significant challenge to both human survival and development, becoming a major issue for the sustainable progress of human society. This paper examines the characteristics and differences in the spatio-temporal distribution of carbon emissions and sinks across Wuhan Region that is stratified into three spatial levels: Hubei Province, the Wuhan Metropolitan Area, and Wuhan City. On a finer spatial scale within Wuhan, it uncovers the spatial correlations and disparities between carbon emissions and sinks. Accordingly, the paper proposes strategies and suggestions for reducing carbon emissions and enhancing carbon sinks at the provincial, metropolitan, and municipal levels. These findings are valuable for sustainable regional development and construction of low-carbon eco-cities. [ABSTRACT FROM AUTHOR]

One of the crucial methods for adapting distributed PV generation is the microgrid. However, solar resources, load characteristics, and the essential microgrid system components are all directly tied to the optimal planning scheme for microgrids. This article conducts a collaborative planning study of grid-connected PV-storage microgrids under electric vehicle integration in various scenarios using HOMER 1.8.9 software. To be more specific, in multiple scenarios, we built capacity optimization models for PV modules, energy storage, and converters in microgrids, with several scenarios each accounting for the cleanliness, economic performance, and overall performance of microgrids. For multiple scenarios, this paper used the net present value cost and levelized cost of electricity as indicators of microgrid economics, and carbon dioxide emissions and the fraction of renewable energy were used as indicators of microgrid cleanliness. The optimal capacity allocation for economy, cleanliness, and a combination of economy and cleanliness were separately derived. Finally, on a business park in Wuhan, China, we conducted thorough case studies to compare and debate the planning performance under various scenarios and to undertake sensitivity analyses on the cases. The sensitivity analyses were conducted for the optimal configuration of microgrids in terms of the EV charging scale, carbon dioxide emissions, PV module unit cost, and storage unit cost. The results of the simulation and optimization show that the optimization approach could determine the ideal configuration for balancing economy and cleanliness. As the EV charging demand increased, the energy storage capacity required in the microgrid gradually increased, while the carbon dioxide emission limit was negatively correlated with the energy storage capacity demand. The unit investment cost of PV module units had a greater impact on the optimal system configuration than the cost of batteries. [ABSTRACT FROM AUTHOR]

This study investigates the degree of spatial correlation between carbon emissions and ecological support coefficients among cities in the middle reaches of Yangtze River (MRYR) region by using social network analysis method and spatial autocorrelation analysis. The actual carbon compensation quota (ACCQ) of each city in MRYR is also calculated based on the carbon revenue and expenditure method, and the theoretical carbon compensation quota (TCCQ) is obtained by introducing technology level and ecological value factors. The results show that (1) carbon sinks in the MRYR region increased very little, while carbon emissions increased significantly during the study period, resulting in an overall carbon deficit in 2015. (2) Wuhan and Changsha, as regional economic centers, are at the center of the carbon emission spatial network, with significant carbon emission spillover. (3) The ecological carrying capacity of the MRYR region showed a spatial and temporal pattern of decline at the center and stability at the periphery. (4) The TCCQ is more convergent in revenues and expenditures than the ACCQ and is more suitable for government policymaking. Based on these findings, we recommend that governments establish regional carbon emission quota systems and horizontal carbon compensation mechanisms based on carbon budget accounting. [ABSTRACT FROM AUTHOR]

Carbon emissions and economic growth are two contradictions in urban development, and their decoupling is related to the sustainable development of cities. This paper took urban agglomeration in the middle reaches of the Yangtze River (UAMRYR), China, as the study area. The Kaya model, the Tapio decoupling model, and the Logarithmic Mean Divisia Index (LMDI) model were adopted to analyze the spatiotemporal differentiation of carbon emissions, the decoupling of economic activities, and driving factors. The results indicate that (1) carbon emissions increased by 66% in the study period, but the growth momentum was curbed after 2015. Low level and medium level areas continue to decrease, and relatively high level area gradually become dominant. (2) Spatially, carbon emissions are in a pattern of middle-hot and east-cold. Jiangxi is in the sub-cold and coldspot area, while the hotspot area is driven by the transformation from Wuhan's single-core to Wuhan and Changsha's dual-core. (3) Since 2010, most cities have been in a good decoupling state, and weak decoupling cities have risen from 35.5% in the initial period to 87.1% in 2010–2011, but the decoupling situation of industrial cities with more high-energy-consuming industries still rebounded slightly. (4) The economic level and energy intensity effect had the most significant impact on the economic decoupling of carbon emissions, whose absolute contribution rates were greater than 35%. Urbanization and economic level both play a positive role in promoting carbon emissions, and the energy intensity plays a negative role in retarding carbon emissions. The population effect was mainly manifested in carbon increase from 2006 to 2011, and 45.2% of the cities from 2011 to 2017 turned into carbon suppression. Finally, we suggest that decoupling carbon emissions from economic growth requires developing green urbanization and a decarbonized economy, optimizing the structure of energy consumption and guiding rational population flow. [ABSTRACT FROM AUTHOR]

With the establishment of carbon peaking and carbon neutrality goals in China, developing low-carbon cities has become crucial. Previous studies primarily focus on the quantitative structure of land use to achieve low carbon optimization, while ignoring the spatial integration between urban form and carbon emissions. To fulfill this gap, this paper proposed a dual-scale low carbon oriented multi-objective land use allocation (DOLA) optimization model, utilizing idea-point multi-objective linear programming (IMLP) and non-dominated sorting genetic algorithm II (NSGA-II) to optimize the low-carbon-emission pattern of the city at both the structural and spatial scales respectively. By innovatively linking the urban shape index with carbon emissions, this model aims to minimize urban carbon emissions, while maximizing economic development, urban development suitability, and the compactness of city. The proposed method is applied to Wuhan's main urban area in China, the DOLA model shows significant improvements in target values across all scenarios. Among them, the scenario that prioritized low carbon emissions at both the structural and spatial scales achieved the most significant reduction of 6.43 % in carbon emissions. The DOLA effectively contributes to achieving multiple spatial targets, thereby aiding in reducing carbon emissions. It offers technical support for developing pathways towards China's "dual carbon" target. • A dual-scale low carbon oriented multi-objective land use allocation (DOLA) optimization model is proposed. • The DOLA model innovatively incorporates urban form indices to carbon emissions. • The DOLA model achieves spatial aggregation targets, thereby facilitating significant reductions in carbon emissions. • The DOLA model generates a series of land use optimization scenarios, offering a valuable reference for land use planning. [ABSTRACT FROM AUTHOR]

Carbon peaking and carbon neutrality in the shipping industry are hot topics but now facing serious challenges. One of them is the current lack of scientific guidance on ship carbon reduction. Precise identification of carbon emission hotspot areas and ship types in ports is the key to controlling carbon emissions from ships. In this paper, a method of identifying and extracting ship carbon emission hotspots based on data field theory is proposed. Based on the theory of field in physics, the spatiotemporal aggregation pattern of ship carbon emissions in the port region is analyzed with regard to the characteristics of ship emission trajectory data. Taking Wuhan Port as an example, we successfully extracted the ship's carbon emission hotspot area based on the proposed method and analyzed the spatial and temporal dynamics of the ship's carbon emission hotspot in the area from several perspectives. The method proposed in this paper can be further applied to coastal ports, which is conducive to exploring the distribution of carbon emission hotspots and the spatial and temporal variation patterns of ships in China's ports. It provides scientific guidance for relevant departments to formulate and implement policies to reduce carbon emissions from ports, and promotes the early realization of the dual carbon goal of China's shipping industry. [ABSTRACT FROM AUTHOR]

PV windows are considered to be a promising building-integrated PV technology due to their excellent electrical, daylighting and thermal performance. However, the shading of the PV cells increases the heating load in winter and the high temperature of PV cells exacerbates the cooling load in summer. Moreover, a significant portion of the heat absorbed by the PV modules is usually wasted. To address these issues, this paper proposed a multi-functional PV/T window. This system could recover the heat on PV cells for producing warm air in winter and hot water in other seasons. As a result, the proposed system can realize the combined PV/T utilization of solar energy and reduce the seasonal thermal needs of the building. First, the mathematical model of the proposed window was developed and validated. Next, the developed model was integrated into building energy software to evaluate the overall energy performance of the proposed window in four different cities of Yangtze River region: Shanghai, Nanjing, Wuhan, and Chongqing in China. These cities experience the subtropical humid climate (Cfa in the Köppen-Geiger classification) and belong to the zones with medium global horizontal irradiation. The sensitivity analysis revealed that the optimal slat angles are between 0o and 40o, while the optimal orientation angles range from 30o to 60o. Selecting low-iron glass as the inner glass resulted in the lowest overall energy consumption. By adopting the MFPV/T window, the annual operation cost of the studied case could be reduced by 34% (6.4 $/m2), 36% (7.0 $/m2), 28% (5.7 $/m2), and 16% (2.9 $/m2) in these four cites, respectively. Additionally, the annual CO 2 emissions could be curtailed by 53.5, 58.5, 47, and 22.3 kg/m2, respectively. • A novel PV/T window was proposed for addressing the common issues of PV windows. • The mathematical model of proposed window was developed and validated. • The parameter sensitivity was analyzed. • The overall energy performance was predicted in Yangtze River region. [ABSTRACT FROM AUTHOR]

In recent years, as China's peaking carbon dioxide emissions and air pollution control projects have converged, scholars have begun to focus on the synergistic mechanisms of greenhouse gas and pollution gas reduction. In 2020, the unprecedented coronavirus pandemic, which led to severe nationwide blockade measures, unexpectedly provided a valuable opportunity to study the synergistic reduction in greenhouse gases and polluting gases. This paper uses a combination of NO2, O3, and CO2 column concentration products from different satellites and surface concentrations from ground-based stations to investigate potential correlations between these monitoring indicators in four Chinese representative cities. We found that XCO2 decreased in March to varying degrees in different cities. It was witnessed that the largest decrease in CO2, −1.12 ppm, occurred in Wuhan, i.e., the first epicenter of COVID-19. We also analyzed the effects of NO2 and O3 concentrations on changes in XCO2. First, in 2020, we used a top-down approach to obtain the conclusion that the change amplitude of NO2 concentration in Beijing, Shanghai, Guangzhou, and Wuhan were −24%, −18%, −4%, and −39%, respectively. Furthermore, the O3 concentration increments were 5%, 14%, 12%, and 14%. Second, we used a bottom-up approach to obtain the conclusion that the monthly averaged NO2 concentrations in Beijing, Shanghai, and Wuhan in March had the largest changes, changing to −39%, −40%, and −61%, respectively. The corresponding amounts of changes in monthly averaged O3 concentrations were −14%, −2%, and 9%. However, the largest amount of change in monthly averaged NO2 concentration in Guangzhou was found in December 2020, with a value of −40%. The change in O3 concentration was −12% in December. Finally, we analyzed the relationship of NO2 and O3 concentrations with XCO2. Moreover, the results show that the effect of NO2 concentration on XCO2 is positively correlated from the point of the satellite (R = 0.4912) and the point of the ground monitoring stations (R = 0.3928). Surprisingly, we found a positive (in satellite observations and R = 0.2391) and negative correlation (in ground monitoring stations and R = 0.3333) between XCO2 and the O3 concentrations. During the epidemic period, some scholars based on model analysis found that Wuhan's carbon emissions decreased by 16.2% on average. Combined with satellite data, we estimate that Wuhan's XCO2 fell by about 1.12 ppm in February. At last, the government should consider reducing XCO2 and NO2 concentration at the same time to make a synergistic reduction. [ABSTRACT FROM AUTHOR]

The low-carbon economy and sustainable development have become a widespread consensus. Chain supermarkets should pay attention to path optimization in the process of distribution to reduce carbon emissions. This study takes chain supermarkets as the research object, focusing on the optimization of the vehicle routing problem (VRP) in supermarket store distribution. Firstly, based on the concept of cost-effectiveness, we constructed a green and low-carbon distribution route optimization model with the lowest cost. With cost minimization as the objective function, the total distribution cost in the vehicle delivery process includes fixed cost, transportation cost, and carbon emission cost. The carbon emission cost is calculated using the carbon tax mechanism. Secondly, through integrating the Floyd algorithm, the nearest neighbor algorithm, and the insertion algorithm, a fusion heuristic algorithm was proposed for model solving, and an empirical study was conducted using the W chain supermarket in Wuhan as an example. The experimental results show that optimizing distribution routes considering carbon emission cost can effectively reduce carbon emissions. At the same time, it can also reduce the total costs of enterprises and society, thereby achieving greater social benefits at lower costs. The research results provide effective suggestions for chain supermarkets to control carbon emissions during the distribution process. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to evaluate the effectiveness of different prefabricated construction (PC) policies using a case study in Wuhan, considering the local context. Design/methodology/approach: The effectiveness of PC policies is falling behind expectations. The main reason lies in an insufficient understanding of the policy impacts. An agent-based model was built by choosing the residential sector in a typical large city of Wuhan, China, as the study case. Different cost reduction scenarios were introduced for investigating the PC policy effectiveness. The proposed model and simulation approach can be used for other cities and generalized to the whole Chinese PC industry with the potential to include more local policies and corresponding data. Findings: Simulation results show that carbon emission reduction will be between 60,000 and 80,000 tons with policy incentives, nearly double that of the no policy intervention scenario. The target of 30% PC in all new buildings by 2026 in China is achievable with the subsidy policies of linear cost reduction, or cost reduction conforms to the learning curve. Practical implications: Simulation results of three kinds of policy show that subsidy policy optimization is necessary regarding reducing the level of subsidy needed. The carbon credit policy is not essential since it has little influence on PC development. Implementing the project procurement restriction policy is not recommended if the scale of development of PC is more important than achieving the development target. Originality/value: This study can help the government and developers make better policy and strategic decisions on PC development and boost the sustainability transition of the construction industry. [ABSTRACT FROM AUTHOR]

Most of the residents' daily travel is concentrated within their 15-min walking distance. In China, derived from the 15-min city concept, the 15-min walkable area is often referred to as the 15-min pedestrian-scale neighborhood, and it has become a basic planning unit. Understanding the factors that influence the built environment of the 15-min pedestrian-scale neighborhood on the residents' daily travel carbon emissions is critical to reduce urban carbon emissions. There may be spatial heterogeneity in daily travel carbon emissions as a dependent variable due to the spatial heterogeneity of built environment factors. Therefore, this study used data from the Wuhan City Resident Travel Survey to describe the spatial pattern of daily travel carbon emissions among Wuhan residents. The study examined the spatial heterogeneity of daily travel carbon emissions and explored the spatial differentiation of the built environment's impact on daily travel carbon emissions within the 15-min pedestrian-scale neighborhood of the residents using spatial autocorrelation analysis and multi-scale geo-weighted regression (MGWR). The results indicate that Wuhan residents' daily travel carbon emissions show an increasing circle structure from the center outward. In general, built environment elements in the 15-min pedestrian-scale neighborhood are closely related to the daily travel carbon emissions, and the direction and degree of impact of the built environment varies spatially. This study provides empirical evidence for controlling transportation carbon emissions. [ABSTRACT FROM AUTHOR]

Quantification and control of NOx and CO2 emissions are essential across the world to limit adverse climate change and improve air quality. We present a new top-down method, an improved superposition column model to estimate day-to-day NOx and CO2 emissions from the large city of Wuhan, China, located in a polluted background. The latest released version 2.3.1 TROPOMI (TROPOspheric Monitoring Instrument) NO2 columns and version 10r of the Orbiting Carbon Observatory-2 (OCO-2)-observed CO2 mixing ratio are employed. We quantified daily NOx and CO2 emissions from Wuhan between September 2019 and October 2020 with an uncertainty of 31 % and 43 %, compared to 39 % and 49 % with the earlier v1.3 TROPOMI data, respectively. Our estimated NOx and CO2 emissions are verified against bottom-up inventories with minor deviations (<3 % for the 2019 mean, ranging from -20 % to 48 % on a daily basis). Based on the estimated CO2 emissions, we also predicted daily CO2 column mixing ratio enhancements, which match well with OCO-2 observations (<5 % bias, within ±0.3 ppm). We capture the day-to-day variation of NOx and CO2 emissions from Wuhan in 2019–2020, which does not reveal a substantial "weekend reduction" but does show a clear "holiday reduction" in the NOx and CO2 emissions. Our method also quantifies the abrupt decrease and slow NOx and CO2 emissions rebound due to the Wuhan lockdown in early 2020. This work demonstrates the improved superposition model to be a promising new tool for the quantification of city NOx and CO2 emissions, allowing policymakers to gain real-time information on spatial–temporal emission patterns and the effectiveness of carbon and nitrogen regulation in urban environments. [ABSTRACT FROM AUTHOR]

The comprehensive optimization analysis of the quantitative structure and spatial allocation of land use is the key research direction of current and future land use planning. There are a lot of uncertain factors in the land use system. However, few studies couple the uncertain factors in the land use system with the spatial layout model. Based on this, the uncertain mathematical model and the spatial allocation model (GeoSOS-FLUS) are coupled to simulate the land use optimal allocation in Wuhan in 2030. The quantitative structure and spatial allocation optimization model of land use under three scenarios of economic development priority, ecological protection priority and low carbon emission priority were predicted. The coupling model solves the quantitative problem of land system uncertainty and applies it to spatial layout. The results show that the coupling model can help decision-makers to make future land use planning from the perspective of economy, ecology and low carbon emissions, and realize the sustainable development of future land use patterns. [ABSTRACT FROM AUTHOR]