Carbon emissions are increasing in the world because of human activities associated with the energy consumptions for social and economic development. Thus, attention has been paid towards restraining ...the growth of carbon emissions and minimizing potential impact on the global climate. Currently there has also been increasing recognition that the urban forms, which refer to the spatial structure of urban land use as well as transport system within a metropolitan area, can have a wide variety of implications for the carbon emissions of a city. However, studies are limited in analyzing quantitatively the impacts of different urban forms on carbon emissions. In this study, we quantify the relationships between urban forms and carbon emissions for the panel of the four fastest-growing cities in China (i.e., Beijing, Shanghai, Tianjin, and Guangzhou) using time series data from 1990 to 2010. Firstly, the spatial distribution data of urban land use and transportation network in each city are obtained from the land use classification of remote sensing images and the digitization of transportation maps. Then, the urban forms are quantified using a series of spatial metrics which further used as explanatory variables in the estimation. Finally, we implement the panel data analysis to estimate the impacts of urban forms on carbon emission. The results show that, (1) in addition to the growth of urban areas that accelerate the carbon emissions, the increase of fragmentation or irregularity of urban forms could also result in more carbon emissions; (2) a compact development pattern of urban land would help reduce carbon emissions; (3) increases in the coupling degree between urban spatial structure and traffic organization can contribute to the reduction of carbon emissions; (4) urban development with a mononuclear pattern may accelerate carbon emissions. In order to reduce carbon emissions, urban forms in China should transform from the pattern of disperse, single-nuclei development to the pattern of compact, multiple-nuclei development.
To reduce carbon dioxide (CO2) emissions attributed widely to human activities, previous studies have paid great attention to the relationships between socioeconomic development, urban forms and CO2 ...emissions in cities, and provided relevant emission mitigation policies through the effective urban spatial planning. However, whether and how different features of urban forms (such as compactness) affecting the levels of CO2 emissions is still debatable, specifically considering the different development levels of the cities. Therefore, this study is to synthetically explore how socioeconomic factors and urban forms work together to affect CO2 emissions with the consideration of differences in development levels of five city tiers in China. First, CO2 emissions in each city were derived from provincial energy statistics, radiance-calibrated nighttime light imageries, and population distribution data based on a disaggregating model. Then, a set of variables representing socioeconomic factors and urban forms were acquired from the city statistics and land use data, respectively. After obtaining the balanced dataset of these five city tiers from 1995 to 2015, the panel data analysis was finally applied to evaluate the consequences of socioeconomic factors and urban forms on CO2 emissions under different development stages. The estimation results show that the economic development, population growth, and urban land expansion are important factors that accelerating CO2 emissions in all the city tiers. Besides, irregular or fragmented structures of urban land use could result in more CO2 emissions due to the increase in potential transportation requirements in all the city tiers. Notably, an increasing concentrated pattern in the urban core is found to increase CO2 emissions in the tier-one cities, but to promote the reduction of CO2 emissions in other four city tiers. The urban spatial development with a compact and multiple-nuclei pattern is suggested to be closely linked with a lower level of CO2 emissions. Such results highlight the importance of a city's development level for decision-making involving the mitigation of CO2 emissions, and provide scientific support for building a low-carbon city from the perspective of both socioeconomic development and urban spatial planning.
•Differentiated impacts of socioeconomic factors and urban forms on CO2 emissions are explored.•Socioeconomic growth and urban land expansion promote CO2 emissions in all city tiers.•Urban forms in different development levels have different impacts on CO2 emissions.•Fragmented patterns of urban land can result in more CO2 emissions in all city tiers.•Compact and centralized developments do little to reduce emissions in tier-one cities.
Recently, the stable light products and radiance calibrated products from Defense Meteorological Satellite Program's (DMSP) Operational Linescan System (OLS) have been useful for mapping global ...fossil fuel carbon dioxide (CO2) emissions at fine spatial resolution. However, few studies on this subject were conducted with the new-generation nighttime light data from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (NPP) Satellite, which has a higher spatial resolution and a wider radiometric detection range than the traditional DMSP-OLS nighttime light data. Therefore, this study performed the first evaluation of the potential of NPP-VIIRS data in estimating the spatial distributions of global CO2 emissions (excluding power plant emissions). Through a disaggregating model, three global emission maps were then derived from population counts and three different types of nighttime lights data (NPP-VIIRS, the stable light data and radiance calibrated data of DMSP-OLS) for a comparative analysis. The results compared with the reference data of land cover in Beijing, Shanghai and Guangzhou show that the emission areas of map from NPP-VIIRS data have higher spatial consistency of the artificial surfaces and exhibit a more reasonable distribution of CO2 emission than those of other two maps from DMSP-OLS data. Besides, in contrast to two maps from DMSP-OLS data, the emission map from NPP-VIIRS data is closer to the Vulcan inventory and exhibits a better agreement with the actual statistical data of CO2 emissions at the level of sub-administrative units of the United States. This study demonstrates that the NPP-VIIRS data can be a powerful tool for studying the spatial distributions of CO2 emissions, as well as the socioeconomic indicators at multiple scales.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
•Land use allocation problem has been encountered in many fields of applications.•Most of land use allocation models ignore macro-level socio-economic variables.•The combination of system dynamics ...(SD) and hybrid particle swarm optimization for land use allocation in this work is new.
Urban land use spatial allocation is crucial to lots of countries that are usually under severe environmental and demographic pressures, because it can be used to alleviate some land use problems. A number of models have been proposed for the optimal allocation of land use. However, most of these models only address the suitability of individual land use types and spatial competition between different land uses at micro-scales, but ignore macro-level socio-economic variables and driving forces. This article proposes a novel model (SDHPSO-LA) that integrates system dynamics (SD) and hybrid particle swarm optimization (HPSO) for solving land use allocation problems in a large area. The SD module is used to project land use demands influenced by economy, technology, population, policy, and their interactions at macro-scales. Furthermore, particle swarm optimization (PSO) is modified by incorporating genetic operators to allocate land use in discrete geographic space. The SDHPSO-LA model was then applied to a case study in Panyu, Guangdong, China. The experiments demonstrated the proposed model had the ability to reflect the complex behavior of land use system at different scales, and can be used to generate alternative land use patterns based on various scenarios.
•An integrated model was proposed to forecast CO2 emissions in cities.•The model was developed by coupling socioeconomic factors and spatial structures.•The city's CO2 emissions were simulated under ...different development strategies.•CO2 abatement can be achieved through urban planning and spatial optimization.
As cities constitute the main sources of CO2 emissions, accurate simulation and prediction of urban CO2 emissions are becoming increasingly necessary for understanding environmental impacts and supporting the policy-making toward a low-carbon development. However, most previous studies on estimating CO2 emissions have only considered the effects of socioeconomic driving factors while disregarding the contributions of urban spatial structures (with the exception of those of urban expansion) to carbon abatement. Therefore, this study presented a model that integrates system dynamics, cellular automata and support vector regression to evaluate the impacts of different socioeconomic developments and urban spatial structures on the CO2 emissions of Guangzhou city. In the integrated model, system dynamics was used to model the developments of socioeconomic variables including urban land-use demand. An artificial neural network cellular automata model based on patch simulation strategy was then built to simulate the urban spatial structures, which were further quantified by landscape metrics. Using both socioeconomic variables and landscape metrics, a support vector regression with polynomial kernel function was finally employed to predict CO2 emissions. Through comparisons drawn between the simulated results and actual data, the integrated model coupling socioeconomic factors and urban spatial structures was demonstrated to be an effective tool for accurately simulating CO2 emissions. Furthermore, scenario simulations derived from the integrated model showed that the scenario of executing moderate population and economic growth, more technological investment, and the compact development of urban spatial pattern constitutes the best development mode for Guangzhou to balance economic growth and CO2 emissions reduction. From these findings, it is suggested that the government should not only develop a series of socioeconomic policies on carbon mitigation but also construct an ideal urban structure of compact and multiple-nuclei development through urban planning and spatial optimization for building a low-carbon city.
The accessibility provided by the transportation system plays an essential role in driving urban growth and urban functional land use changes. Conventional studies on land use simulation usually ...simplified the accessibility as proximities and adopted the grid-based simulation strategy, leading to the insufficiencies of characterizing spatial geometry of land parcels and simulating subtle land use changes among urban functional types. To overcome these limitations, an Accessibility-interacted Vector-based Cellular Automata (A-VCA) model was proposed for the better simulation of realistic land use change among different urban functional types. The accessibility at both local and zonal scales derived from actual travel time data was considered as a key driver of fine-scale urban land use changes and was integrated into the vector-based CA simulation process. The proposed A-VCA model was tested through the simulation of urban land use changes in the City of Toronto, Canada, during 2012-2016. A vector-based CA without considering the driving factor of accessibility (VCA) and a popular grid-based CA model (Future Land Use Simulation, FLUS) were also implemented for comparisons. The simulation results reveal that the proposed A-VCA model is capable of simulating fine-scale urban land use changes with satisfactory accuracy and good morphological feature (kappa = 0.907, figure of merit = 0.283, and cumulative producer's accuracy = 72.83% ± 1.535%). The comparison also shows significant outperformance of the A-VCA model against the VCA and FLUS models, suggesting the effectiveness of the accessibility-interactive mechanism and vector-based simulation strategy. The proposed model provides new tools for a better simulation of fine-scale land use changes and can be used in assisting the formulation of urban and transportation planning.
Fine knowledge of the spatiotemporal distribution of the population is fundamental in a wide range of fields, including resource management, disaster response, public health, and urban planning. The ...United Nations’ Sustainable Development Goals also require the accurate and timely assessment of where people live to formulate, implement, and monitor sustainable development policies. However, due to the lack of appropriate auxiliary datasets and effective methodological frameworks, there are rarely continuous multi-temporal gridded population data over a long historical period to aid in our understanding of the spatiotemporal evolution of the population. In this study, we developed a framework integrating a ResNet-N deep learning architecture, considering neighborhood effects with a vast number of Landsat-5 images from Google Earth Engine for population mapping, to overcome both the data and methodology obstacles associated with rapid multi-temporal population mapping over a long historical period at a large scale. Using this proposed framework in China, we mapped fine-scale multi-temporal gridded population data (1 km × 1 km) of China for the 1985–2010 period with a 5-year interval. The produced multi-temporal population data were validated with available census data and achieved comparable performance. By analyzing the multi-temporal population grids, we revealed the spatiotemporal evolution of population distribution from 1985 to 2010 in China with the characteristic of concentration of the population in big cities and the contraction of small- and medium-sized cities. The framework proposed in this study demonstrates the feasibility of mapping multi-temporal gridded population distribution at a large scale over a long period in a timely and low-cost manner, which is particularly useful in low-income and data-poor areas.
Rapid urbanization has profoundly impacted social and environmental systems. China is the world's largest developing country. Projecting future urban expansion in China is critical to alleviate any ...adverse impacts and achieve sustainable development goals. However, the existing national-scale urban expansion simulations at low or medium resolutions produce significant distortions in urban spatial patterns, limiting the utility of future projections. High-resolution simulation of urban expansion is challenging on a large scale owing to high computational demands. In this study, we used a high-performance cellular automata model (Tensor-FLUS) to simulate the urban expansion of China from 2015 to 2050, at 30 m spatial resolution, under shared socioeconomic pathways. The high-resolution (30 m) urban expansion simulations preserve greater spatial details and avoid 34.07-37.60% underestimation of the urban area, compared with simulation at 1 km resolution. The environmental impact assessments revealed that future urban expansion mainly encroaches on cropland (88.00-88.29%), with a greater likelihood of occupying productive cropland, placing substantial pressure on food production. Although the proportion of occupied natural land is relatively small (11.32-11.60%), newly expanded urban areas will tend to consume woodland and grassland of high ecological value, leading to profound impacts on the ecosystem. In general, the produced high-resolution future simulations can reduce the uncertainty of environmental impact assessment at the national scale. Furthermore, it can provide consistent projection data for research at the provincial or metropolitan scales to support urban planning and local climate change mitigation.
•The impact of socioeconomic factors, urban form, and transportation networks on CO2 emissions is investigated.•An extended STRIPAT model is used, taking the period 1990–2010.•Socioeconomic factors ...correlates significantly with CO2 emissions.•Pursuing compact urban development patterns would help to reduce CO2 emissions.•Improving the coupling degree of urban spatial structure and traffic organization can reduce CO2 emissions.
In addition to socioeconomic factors, urban planning and transportation organization are beginning to play an increasingly important role in the reduction of CO2 emissions. However, little attention has been paid to the ways in which this emerging role can be framed. Therefore, this study aims to examine the combined impacts of socioeconomic and spatial planning factors on CO2 emissions in cities that have experienced rapid urbanization, using an econometric model and a comprehensive panel dataset incorporating socioeconomic, urban form, and transportation factors for four Chinese megacities—Beijing, Tianjin, Shanghai and Guangzhou, —in the period 1990–2010. Making use of remote sensing land-use data, the digitization of transportation maps, and a set of socioeconomic data, we developed an extended STRIPAT model in order to empirically estimate the impacts of the selected variables on CO2 emission levels in these cities. The results indicate that the socioeconomic factors of economic growth, urbanization, and industrialization will lead to increased CO2 emissions, while the service level and technology level can contribute to the reduction of CO2 emissions. The results also suggest that the expansion of urban land use and increases in urban population density should be controlled through urban planning measures in order to reduce CO2 emissions. In addition, pursuing compact urban development patterns would also help to reduce CO2 emissions. Transportation factors including urban road density and the traffic coupling factor were both found to have exerted significant negative effects on CO2 emission levels, indicating that increases in the coupling degree between urban spatial structure and traffic organization can also contribute to reducing such emissions. Our results cast a new light on the importance of practices of urban planning and spatial optimization measures in achieving CO2 emission reductions. The findings obtained in this study are seen as providing important decision support in building low-carbon cities in China.
In the field of building extraction, many CNN-based methods have been developed to solve the problem of the irregular boundaries in their predictions. The prevailing approach is to build an ...additional edge segmentation branch or obtain accurate vector components of buildings. However, pixel-based methods still cannot obtain accurate location of the boundary, while vector extraction will bring the problem of sample imbalance and missing detection. In this work, we utilize the complementarity of the two types of methods and propose the line segment collaborate segmentation (LCS) framework. In the proposed LCS framework, semantic segmentation provides location guidance for vector extraction, while vector extraction provides precise positioning for semantic segmentation. By this way, the two tasks can leverage their respective strengths. The results on three datasets show that the performance of vector extraction and semantic segmentation is improved simultaneously using the LCS framework, which proves the effectiveness of our method. At the same time, our framework is flexible and can be embedded in other vector extraction methods to improve performance.
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