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Mapping changes in carbon emissions and carbon storage (CECS) with high precision at a small scale (urban street-block level) can improve governmental policy decisions with respect to the construction of low-carbon cities. In this study, a methodological framework for assessing the carbon budget and its spatiotemporal changes from 2015 to 2017 in W...
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Context 1
... carbon emission factors in this study were calculated based on the default values of carbon contents and default net calorific values that were obtained from the IPCC guidelines for national greenhouse gas inventories. Table 2 shows the calculated carbon emission factors for each type of fuel. In this study, the carbon emissions of industrial production processes were estimated by calculating the carbon dioxide emissions during the production of cement, steel, and other materials. ...
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... contrast, the carbon emissions that were generated by urban domestic regions and transportation increased from 2015 to 2017, mainly due to economic development and population growth. For total carbon emissions (Table 2), industrial carbon emissions are the largest contributors, accounting for 54-60%, followed by urban living carbon emissions, which account for 37-42% of the total. Agricultural carbon emissions are the smallest, accounting for only 0.17-0.19% of the total in Wuhan. ...
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... Understanding wood C density (CD) is the basis for measuring and analyzing C storage in a regional forest ecosystem. Wood CD is usually obtained by an assumed C concentration (0.45 or 0.50 g/g) multiplied by a constant wood density (WD) [12,13]. However, the estimated CD has a deviation of about 10% relative to the actual observed values at tree level [14][15][16]. ...
Wood can store carbon and help mitigate global climate change. Carbon density (CD), the basis for measuring and analyzing C storage, is the product of wood density (WD) and C concentration, which are dependent on wood structure, cellulose concentration (CC), hemicellulose concentration (HC), and lignin concentration (LC). However, little attention has been paid to the C concentration of cellulose, hemicellulose, and lignin, which are fundamental factors in C storage and affect the credibility of accurate CD estimates. In order to disentangle the CD drives, WD, C concentration, CC, HC, and LC of the branch, stem, and root were quantified for five Rosaceae species from temperate forests in Northeastern China. The species were Sorbus alnifolia (Sieb.et Zucc.) K. Koch, Pyrus ussuriensis Maxim., Malus baccata (L.) Borkh., Crataegus pinnatifida var. major N. E. Brown, and Padus racemosa (Linn.) Gilib. The WD, CC, HC, and LC differed among species and tree organs, with the highest variability for the HC. The structural carbon concentration (SCC) was lower than the organic carbon concentration (OCC) and even the Intergovernmental Panel on Climate Change (IPCC) default value of 45%, with a maximum deviation of 2.6%. CD differed dramatically among species and tree organs. Based on SCC calculations, the highest CD was found in Sorbus alnifolia root (0.27 × 106 g/m3), while the lowest was found in Padus racemosa branch (0.22 × 106 g/m3). The results suggest that when estimating CD accurately at species level, it is important to consider not only WD but also structural carbohydrates and lignin concentration, providing important information on C fluxes and long-term C sequestration for forests. The study findings provide valuable insights into CD variations among tree species and organs and are valuable for forest management and policy development to improve carbon sequestration.
... Previous land-use dynamic-based models for predicting carbon emission can simulate highspatiotemporal-resolution results of carbon emissions by using multisource data integration (Dou et al., 2022), carbon emission coefficients (Zhou et al., 2021), or uncertainty in observation constraints (Lienert & Joos, 2018). They can provide a more detailed basis for urban carbon emissions management (Jia et al., 2020;Liu et al., 2019). However, two problems with these models constrain modern carbon management (Lai et al., 2016). ...
Forecasting cities' carbon emissions is an essential support for peak carbon emissions. Previous studies have mainly focused on estimating carbon emissions at large regional scales. Higher spatial resolution mapping of carbon emissions and simulation of future scenarios are important to support locally appropriate policy guidance to reduce carbon emissions. This paper proposes a bottom-up cadastral parcel-scale Carbon emission forecasting framework based on Vector-based Cellular Automata (CarbonVCA) by integrating land use modelling and carbon emission estimation. Shenzhen's cadastral parcel-scale carbon emissions from 2020 to 2060 are predicted as a case study. A good performance was achieved using CarbonVCA (MAPE = 19.017 %, RMSE = 0.175 Mtpa (C)). Three progressive scenarios are designed for carbon emission simulation from land use planning and energy structure restructuring perspectives. The designed carbon emission reduction scenario limits carbon emissions and enables Shenzhen to achieve peak carbon emissions between 2025 and 2030. However, the efforts to reach peak carbon emissions may prompt the relocation of industrial land to the suburbs. Such areas will need appropriate infrastructure construction to break through terrain and landscape constraints, maintain economic growth and achieve sustainable development. This framework can forecast a high spatial resolution of land-use-based carbon emissions, which helps construct low-carbon cities.
... At the city level, research on the relationship between LUCCs and the carbon balance has found that the overall carbon balance level of most cities is in a deficit [29][30][31][32][33][34], and a carbon balance cannot be achieved within the urban system. However, the scale involved in national-level studies is too wide, and it not only ignores the dynamic characteristics of urban development but also makes it difficult to reveal the microscopic changes in land use and the differential effects of carbon emissions on NSCE. ...
The implementation of carbon peaking and carbon neutrality is an essential measure to reduce greenhouse gas emissions and actively respond to climate change. The net carbon sink efficiency (NCSE), as an effective tool to measure the carbon budget capacity, is important in guiding the carbon emission reduction among cities and the maintenance of sustainable economic development. In this paper, NCSE values are used as a measure of the carbon budget capacity to measure the spatiotemporal evolution of the carbon neutral capacity of three major urban agglomerations (UAs) in China during 2007–2019. The clustering characteristics of the NCSE of these three major UAs, and various influencing factors such as carbon emissions, are analyzed using a spatiotemporal cube model and spatial and temporal series clustering. The results reveal the following. (1) From the overall perspective, the carbon emissions of the three major UAs mostly exhibited a fluctuating increasing trend and a general deficit during the study period. Moreover, the carbon sequestration showed a slightly decreasing trend, but not much fluctuation in general. (2) From the perspective of UAs, the cities in the Beijing–Tianjin–Hebei UA are dominated by low–low clustering in space and time; this clustering pattern is mainly concentrated in Beijing, Xingtai, Handan, and Langfang. The NCSE values in the Yangtze River Delta UA centered on Shanghai, Nanjing, and the surrounding cities exhibited high–high clustering in 2019, while Changzhou, Ningbo, and the surrounding cities exhibited low–high clustering. The NCSE values of the remaining cities in the Pearl River Delta UA, namely Guangzhou, Shenzhen, and Zhuhai, exhibited multi-cluster patterns that were not spatially and temporally significant, and the spatiotemporal clusters were found to be scattered. (3) In terms of the influencing factors, the NCSE of the Beijing–Tianjin–Hebei UA was found to be significantly influenced by the industrial structure and GDP per capita, that of the Yangtze River Delta UA was found to be significantly influenced by the industrial structure, and that of the Pearl River Delta UA was found to be significantly influenced by the population density and technology level. These findings can provide a reference and suggestions for the governments of different UAs to formulate differentiated carbon-neutral policies.
... For example, the cities are distinguished by the high concentration of vehicles energy consumption, emission, population, and industries. To minimize the GHG and, in particular, the fuel energy consumption by developing alternative energy sources that are inexpensive and which increase the vehicle efficiency, better planning is desired [15]. Emission factor is typically expressed in gram per liter fuel burnt with the contextual information on vehicle fuel, model, and driving conditions [16]. ...
Trackers installed in vehicles gives insights into many useful information and predict future mobility patterns and other aspects related to vehicles movement which can be used for smart and sustainable cities planning. A novel approach is used with the COPERT model to estimate fuel consumption on a huge dataset collected over a period of one year. Since the data size is enormous, Apache Spark, a big data analytical framework is used for performance gains while estimating vehicle fuel consumption with the lowest latency possible. The research presents peak and off-peak hours fuel consumption’s in three major cities, i.e., Karachi, Lahore and Islamabad. The results can assist smart city professionals to plan alternative trip routes, avoid traffic congestion in order to save fuel and time, and protect against urban pollution for effective smart city planning. The research will be a step towards Industry 5.0 by combining sustainable disruptive technologies.
... The Eulerian model has been used to observe the air or to generate a high-precision time-space distribution map using a comprehensive coefficient model of the carbon density factor and the corresponding land cover type. It is necessary for the government to understand the spatial and temporal distributions of carbon emissions and take corresponding measures to reduce carbon emissions to make the energy economy sustainable [21][22][23][24]. ...
Faced with the deterioration of the environment and resource shortages, countries have turned their attention to renewable energy and have actively researched and applied renewable energy. At present, a large number of studies have shown that renewable energy can effectively improve the environment and control the reduction of resources. However, there are few studies on how renewable energy improves the environment through its influencing factors. Therefore, this paper mainly analyses the relationship between wind energy and carbon emissions in renewable energy and uses Chinese data as an example for the case analysis. Based on the model and test methods, this paper uses the 1990–2018 data from the China Energy Statistical Yearbook to study and analyse the correlation between wind energy and carbon emissions and finally gives suggestions for wind energy development based on environmental improvements.
Cultural landscapes provide abundant and diverse ecosystem services (ES) for human-wellbeing. However, many traditional cultural landscapes worldwide are currently undergoing rapid urbanization. In decision-making concerning sustainable urbanization, tradeoffs frequently occur between different objectives (i.e., between multiple ES) and between different pathways or urbanization strategies (e.g., following modern zoning principles or traditional landscape structures). This study aims to examine the dynamics and interactions between multiple ES under different strategies for the urbanization of cultural landscapes. A case study was conducted in Nansha, China. Three scenarios—business-as-usual, zoning plan-based, and traditional landscape structure-based—were developed to reflect the most common urbanization strategies, each parameterized with identical land-use quantities. Land-use change from 2020 to 2035 under different scenarios was simulated using the PLUS model (integrated Random Forest and Cellular Automata models). The traditional landscape structure-based scenario used the settlement pattern before urbanization to predict the chances of future urban areas’ occurrence. Eleven ES indicators were used to examine ES dynamics and interactions in the simulation outcomes. The results showed that the amount of ES provided by the landscape declined and significant tradeoffs occurred between cultural and non-cultural ES. The business-as-usual scenario resulted in the greatest decrease in ES. The zoning plan-based scenario did not offer a significant improvement over the business-as-usual scenario. The traditional landscape structure-based scenario was the most effective in limiting ES decline, which also mitigated the tradeoff between urban development and flood regulation and fostered synergy between urban efficiency and ecotourism opportunity. Based on these findings, we recommend that traditional landscape structures should be emphasized in the development of cultural landscapes.
Buildings are key society element and essential in the formation of sustainable cities. Several studies focused on life cycle assessment of construction materials while limited research analyzed CO2 emissions associated with buildings. This research proposes a spatio-temporal framework to analyze CO2 emissions of buildings applied at the city scale to assist in planning and decision-making of buildings construction and operation. The embedded methods within the framework can identify locations of high-intensity CO2 emissions in a city and recommend retrofitting actions to resolve these hotspots. The framework analysis is applied to the city of Ajman in the United Arab Emirates that had witnessed major expansion. The findings indicate that the embodied carbon emissions equivalents (ECEE) for buildings construction had increased from 7,095 Kt to 9,064 Kt, and then to 12,680 Kt in the years 1986, 2005, and 2015, respectively, and the ECEE for buildings operation had increased from 1,389 Kt/year to 1,919 Kt/year, and then to 2,547 Kt/year in the years 1986, 2005, and 2015, respectively. The recommended retrofitting actions for Ajman developed through the analysis can reduce total CO2 emissions of buildings operation by 14%. Future work can consider additional sustainability indicators within the proposed framework.
