Low-carbon Urban Planning Research Articles

Strategizing Low Carbon Urban Planning Through Environmental Impact Assessment by Artificial Intelligence Driven Carbon Foot-print Forecasting

Addressing the associated rise in Carbon Emissions (CE) as smart cities expand becomes paramount. Effective low-carbon urban planning demands robust, precise assessments. This research introduces a cutting-edge solution via an Artificial Intelligence (AI) -driven Carbon Footprint (CF) impact assessment. A detailed dataset, collected over 3 years, was harnessed to gather insights into vital urban factors, including CE, Energy Consumption (EC) patterns, variations in land use, transportation dynamics, and changes in air quality. The cornerstone of this research is developing the Multi-modal Stacked VAR-LSTM model. This model proposes to provide accurate CF predictions for urban environments by merging the capabilities of Vector Autoregression (VAR) with Long Short-Term Memory (LSTM) neural networks. The process encompasses dedicated assessments for each data segment, harnessing VAR to delineate interdependencies and refining these predictions with the LSTM network using the residuals from the VAR analysis. By interweaving AI-driven carbon footprint impact assessments into the urban planning discourse, this study underscores the vast potential in sculpting future urban development strategies that are sustainable and sensitive to carbon impact.

Construction of urban low-carbon development and sustainable evaluation system based on the internet of things

Low-carbon (LC) cities are the cities that people long for today. LC environmental protection plays a very important role in people's health. The construction of a city in LC is a great cause that contributes to the present and benefits the future. In this study, we propose a development and sustainability evaluation system for building low-carbon cities based on the Internet of Things (IoT). The study is novel in that it considers key areas such as urban planning, environmental issues and solutions, and how the Internet of Things can optimize low-carbon logistics and smart grids, with the aim of promoting the formation of low-carbon city models. This comprehensive approach not only presents problems and solutions in low-carbon urban planning but also focuses on how the Internet of Things can be used as a key technology to promote low-carbon urban development. The urban development of LC was constructed with a sustainable evaluation system, so that people could experience the life of LC. Through the investigation of the degree of atmospheric pollution of LC cities using the Internet of Things, this article found that the highest degree of atmospheric pollution was 30. The highest degree of atmospheric pollution in cities in LC without IoT was 53. The severity of water pollution in cities in LC using IoT technology ranged from 10 to 25, while those without IoT ranged from 30 to 60. The degree of soil pollution in LC cities using IoT technology was concentrated in 10–30, while those without using IoT were concentrated in 30–50. Through these experimental data, it could be seen that IoT technology could reduce environmental pollution, thus achieving the effect of LC cities. This shows that the use of IoT technology in LC cities was highly feasible.

Spatial modelling of street-level carbon emissions with multi-source open data: A case study of Guangzhou

A refined spatial understanding of carbon emissions is crucial for advancing low-carbon development. This study aims to develop a comprehensive, open data-based approach for spatial modelling of carbon emissions at street level, covering five sectors: industry, transportation, residential & public service, commerce, and agriculture in Guangzhou. Two sets of open data, including statistical yearbook data and urban morphology data, were analyzed using a comprehensive methodology that integrates both bottom-up and top-down approaches to map the spatial distribution of carbon emissions. The findings delineate the carbon emission hierarchy across five distinct sectors as follows: Industrial (37.9%), Transportation (31.3%), Residential (18.6%), Commercial and Public Services (11.6%), and Agriculture (0.6%). The industrial sector emerges as the largest contributor, emitting 61.59 million tons, chiefly situated in suburban industrial zones like Huangpu and Panyu. Following closely is transportation, emitting 50.87 million tons, concentrated around Baiyun International Airport, ports, and urban areas with heavy traffic. Commercial and residential sectors emit 18.94 million tons, primarily within densely populated areas such as Tianhe and Haizhu. Agricultural emissions total 1.02 million tons, predominantly located on the city's outskirts, notably in Nansha. The findings of this study could provide information support for identifying carbon emission hotspots and developing sector-specific low-carbon urban planning strategies.

Bayesian deep neural network for low-carbon scheduling strategy planning of V2G

Abstract With the increasing adoption of electric vehicles, the Vehicle-to-Grid (V2G) model has become crucial in integrating renewable energy generation. However, challenges exist in developing grid scheduling strategies that are tailored to different regions, as well as in quantifying the economic benefits and carbon emissions associated with such scheduling. To address these issues, our study proposed a novel V2G low-carbon scheduling strategy planning method based on Bayesian neural networks. Initially, we established a stochastic V2G model that incorporated grid and electric vehicle scheduling, along with a mathematical model that captured the random behavior of EV users, enabling us to capture the essential characteristics of scheduling planning. Subsequently, we employed an enhanced Bayesian deep neural network to learn and assimilate these scheduling planning characteristics, allowing for the provision of a grid scheduling strategy that ranks economic benefits based on weighted priorities. Furthermore, we conducted simulation experiments within the coverage area of the Internet of Things in Energy (IoTE) to gather scheduling characteristics. The experimental results demonstrated that our method outperformed other deep learning models in terms of voltage amplitude stability during the grid scheduling process, showcasing superior robustness and accuracy. Moreover, we evaluated the economic benefits of the scheduling model and compared it with the original V2G scheduling model. The findings revealed that our model exhibited higher economic benefits and lower carbon emissions. Considering the future challenges of low-carbon urban planning, our method holds significant potential in terms of grid scheduling economic benefits and carbon emission indicators.

Predicting building age from urban form at large scale

To stay within 1.5 °C of global warming, reducing energy-related emissions in the building sector is essential. Rather than generic climate recommendations, this requires tailored, low-carbon urban planning solutions and spatially explicit methods that can inform policy measures at urban, street and building scale. Here, we propose a scalable method that is able to predict building age information in different European countries using only open urban morphology data. We find that spatially cross-validated regression models are sufficiently robust to generalize and predict building age in unseen cities with a mean absolute error (MAE) between 15.3 years (Netherlands) and 19.9 years (Spain). Our experiments show that large-scale models improve generalization for predicting across cities, but are not needed to infer missing data within known cities. Filling data gaps within known cities is possible with a MAE between 9.6 years (Netherlands) and 16.7 years (Spain). Overall, our results demonstrate the feasibility of generating missing age data in different contexts across Europe and informing climate mitigation policies such as large-scale energy retrofits. For the French residential building stock, we find that using age predictions to target retrofit efforts can increase energy savings by more than 50% compared to missing age data. Finally, we highlight challenges posed by data inconsistencies and urban form differences between countries that need to be addressed for an actual roll-out of such methods.

Low-carbon city planning based on collaborative analysis of supply and demand scenarios

With the proposal of carbon reduction goals of countries around the world, "green and low-carbon" has become a new key to urban development and construction. However, in the existing urban planning system, the energy planning mapped by carbon emissions has received less attention, and the main goal is often to ensure safe supply. There is less research on carbon emissions. Therefore, it is necessary to take the energy planning in urban planning as the breakthrough point to carry out research on green and low-carbon development and construction at the urban level in the " Carbon Reduction " era. First, a city in China was taken as the research object to evaluate the resource endowment and development potential of the target city. Then, the carbon reduction paths of target cities in the three fields of construction, transportation and industry were studied, and the carbon reduction potential in different scenarios was quantified. After that, the influence of electric power carbon emission coefficient on different carbon saving scenarios was studied through sensitivity analysis. Finally, the government's low-carbon urban planning and decision-making suggestions were given. The results show that the relatively effective way is to carry out large-scale promotion of ultra-low energy consumption buildings and electric vehicles on the demand side, and to build distributed photovoltaic and heat pump systems on the supply side. On this basis, with the introduction of additional 30% of foreign green power, the per capita carbon emissions of cities would be reduced to 1.5tCO2 in 2035. In this way, the target city can achieve the goal of carbon neutrality in advance. Therefore, the results of this study have certain theoretical reference value for the study of low-carbon urban planning.

From Policy to Implementation—An Analytic Network Process (ANP)-Based Assessment Tool for Low Carbon Urban and Neighborhood Planning

To achieve the goals of carbon peaking, a national policy instrument for carbon peaking in the building and construction sector has been launched by the Chinese MOHURD (30 June 2022). We have developed an actionable framework for implementing these policy objectives. The framework was designed by classifying and prioritizing selected strategic government recommendations in the form of an interactive indicator system and tool for evaluating the quality of low-carbon urban and neighborhood planning actions based on the decarbonation principles of carbon emission reduction and carbon capture. The analytic network process (ANP) was applied for processing the interactions and prioritizing the indicators (23 in total for the two principles applied). A scorecard was designed for assessing low-carbon urban and neighborhood planning strategies and technologies. The practical implementation of the tool was then tested with two real planning cases, one from a fourth-tier Chinese city and another from a high-density city. The applicability of the tool is further discussed by comparing it with well-developed international assessment tools in other contexts. This article contributes to the literature by first initiating research on the use of this evaluative tool for low-carbon planning and secondly by demonstrating how researchers can convert policies into practical implementations.

What urban spatial structure is more conducive to reducing carbon emissions? A conditional effect of population size

Clarifying what urban spatial structure (US) is more conducive to reducing emissions and conserving energy provides scientific references for government departments and decision-makers to optimize and realize their “carbon reduction” goal. In light of the mixed results of previous studies, our study explored the effect of monocentric US (MUS) and polycentric US (PUS) on carbon emissions in China by using panel regression models and robust test methods based on an US quantification through remotely sensed nighttime light data. According to the findings, population size regulates the effect of US on carbon emissions. For cities with a population size less than 70.71 × 104–98.58 × 104, a MUS is more conducive to reducing carbon emissions, whereas for cities with a population size large than 70.71 × 104–98.58 × 104, a PUS performs better on carbon emission reduction. Further quantitative analyses reveal that the conditional effect of population size on the US—carbon emissions relationship is dependent on the relative magnitudes of the three mediating factors, e.g., the transportation, residents' lives, and industrial production and manufacturing. Our study determines the MUS and PUS most conducive to carbon emission reduction for different population sizes, which may lay the groundwork for a low-carbon urban planning system.

The impact of urban morphology on the spatiotemporal dimension of estate-level air temperature: A case study in the tropics

This research evaluated the spatiotemporal dimension of the thermal environment of urban microclimate system in Singapore through a case study. To provide a holistic assessment of air temperature due to urban morphology, for the spatial dimension, correlations between different urban morphology variables and estate-level air temperature were examined through machine learning models to test nonlinear assumption. Different urban morphologies were measured and calculated including shading coefficient of buildings, shading coefficient of greenery, green plot ratio, building height, building cover ratio, building wall area, impervious ground surface fraction, and ground albedo. The temporal dimension of air temperature changes was examined at hourly resolution. The test score of hourly temperature predictions using nonlinear algorithm ranged from 0.24 °C to 0.67 °C, with RMSE above 0.5 °C occurring at T8-T17. Furthermore, this research aggregated hourly temperature prediction into an average cumulative temperature increase (ACTI). A heatmap was created showing ACTI on a university campus with a maximum temperature difference of 2 K between cold and hot spots. Based on the investigation, this study further proposed guidelines for low carbon urban planning for greenery, painting or pavement material, and active system.

Urban Form and Function Optimization for Reducing Carbon Emissions Based on Crowd-Sourced Spatio-Temporal Data.

The low-carbon city has become an important global urban development-oriented goal. One important aspect of urban space is low-carbon urban planning, which has a vital role in urban carbon emissions. Which types of urban form and function allocations are conducive to reducing carbon emissions is therefore a key issue. In this study, the Futian and Luohu Districts of Shenzhen, Guangdong Province, China, are taken as an example to investigate this issue. Firstly, a “head/tail” breaks method based on the third fractal theory is adopted to obtain the minimum evaluation parcel of urban space. Then, the Landscape Shape Index (LSI), Fragmentation Index (C), Shannon’s Diversity Index (SHDI), and Density of Public Facilities (Den) are used to evaluate the form and function allocation of each parcel. In addition, the CO2 concentration distribution in this study area is acquired from remote sensing satellite data. Finally, the relationships between urban form, function allocation, and CO2 concentration are obtained. The results show that the lower the urban form index or the higher the urban function index, the less the CO2 concentration. To verify this conclusion, three experiments are designed and carried out. In experiment A, the CO2 concentration of the tested area is reduced by 14.31% by decreasing the LSI and C by 6.1% and 9.4%, respectively. In experiment B, the CO2 concentration is reduced by 15.15% by increasing the SHDI and Den by 16.3% and 12.1%, respectively. In experiment C, the CO2 concentration is reduced by 27.72% when the urban form and function are adjusted in the same was as in experiments A and B.

Effects of Urban Form on Carbon Emissions in China: Implications for Low-Carbon Urban Planning

Carbon emissions are closely related to global warming. More than 70% of global carbon emissions have been generated in cities. Many studies have analyzed the effects of cities on carbon emissions, from the perspective of urbanization, economics, and land use, yet a detailed understanding of the relationship between urban form and carbon emissions is lacking due to the absence of a reasonable set of urban form metrics. The aim of this research is to explore the effects of urban form on carbon emissions through empirical research. By eliminating collinearity, we established a set of urban form landscape metrics comprising Class Area (CA), Mean Perimeter–Area Ratio (PARA-MN), Mean Proximity Index (PROX-MN), and Mean Euclidian Nearest Neighbor Distance (ENN-MN) representing urban area, complexity, compactness, and centrality, respectively. Through spatial autocorrelation analysis, the results show that there is a positive spatial autocorrelation of carbon emissions. The high–high agglomeration regions are located in the Beijing–Tianjin–Hebei and Yangtze River Delta, while the low–low agglomeration regions are concentrated in the Southwest and Heilongjiang Province. Based on a spatial error model, for the whole study area, CA, PARA-MN, and ENN-MN show a positive correlation with carbon emissions, but PROX-MN is the opposite. Based on ordinary least squares, PARA-MN in the Northeast and East, PROX-MN in the North and Mid-South, and ENN-MN in the North are significantly correlated with carbon emissions. These findings are helpful for low-carbon urban planning.

Analysis and Evaluation of Factors Influencing the Low-Carbon Effect of Urban High-Rise Settlement Planning Schemes Based on AHP-Fuzzy Comprehensive Evaluation Method

With the rise of low-carbon concept and the trend of low-carbon planning, the evaluation of low-carbon planning has been paid more and more attention by the academic and planning circles. Carbon evaluation has become a hot topic after “environmental evaluation” and “green evaluation.” In order to promote the important content of energy conservation and sustainable development in the construction of low-carbon cities, this paper constructs and plans the content of low-carbon city planning and construction. On the basis of the effectiveness evaluation index system, this paper discusses the linkage between analytic hierarchy process and fuzzy comprehensive evaluation method and makes a comprehensive quantitative evaluation on the effectiveness of case planning EIA. Combining the advantages of analytic hierarchy process and fuzzy comprehensive evaluation method, the subobjectives and weights of each index are determined by the analytic hierarchy process. The multi-level fuzzy comprehensive evaluation method is used to evaluate the factors that affect the low-carbon effect of urban high-rise residential district planning. This study leads the new trend of future urban construction, and the construction of low-carbon residential areas, as an important part of promoting urban sustainable development, is the core component of future low-carbon urban planning and construction.

Towards low-carbon cities through building-stock-level carbon emission analysis: a calculating and mapping method

• A bottom-up method is proposed to calculate and map carbon emissions from urban building stock. • Building types and geometry are extracted to derive life cycle carbon emission inventories. • The calculation result is verified with a modified top-down method. • The method improves calculation accuracy and facilitates low-carbon urban planning. Building stock represents the primary source of urban carbon emissions. However, the amount and spatial distribution of building-stock-level carbon emissions remain unclear when formulating detailed carbon emission reduction goals for cities. In present study, a bottom-up method was established for calculating and mapping carbon emissions from urban building stock, which involves: (1) using inventory data to derive an aggregated reference model for calculating urban building carbon emissions and sinks with a building life cycle assessment model and a building archetype method; and (2) mapping and assessing carbon emissions from building stock to support low-carbon urban planning. The method was applied in Xi'an, China. The embodied carbon emissions from building stock in Xi'an were calculated as 4.13 million tCO 2 e·a –1 , the operational emissions as 20.45 million tCO 2 e·a –1 , building and natural carbon sinks as 0.067 million tCO 2 e·a –1 and 0.014 million tCO 2 e·a –1 , respectively. The method was validated with municipal statistical data for Xi'an. In addition, carbon emissions from urban building stock were mapped to identify carbon reduction hot spots. The proposed method allows municipalities to assess the environmental performance of urban building stock and urban planning schemes, thereby supports the sustainable development of cities.

县域城镇空间形态与居住建筑能耗关联性的连续尺度研究——以浙江长兴、福建连江为例

PDF HTML阅读 XML下载 导出引用 引用提醒 县域城镇空间形态与居住建筑能耗关联性的连续尺度研究——以浙江长兴、福建连江为例 DOI: 10.5846/stxb202103280807 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: “十三五”国家重点研发计划课题"（2018YFC0704705）；福建省中青年教师教育科研项目（JAT190034）；福州大学引进人才科研启动项目（511005-GXRC-21024） Correlation between urban spatial form and residential building energy use of towns on a continuous scale: A case of Changxing, Zhejiang and Lianjiang County, Fujian Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:居住建筑能耗受建筑周边空间形态的直接影响，确定空间形态低碳效应及其最佳尺度将有助于实现县域城镇低碳转型。以浙江省长兴县、福建省连江县为例，共计选取49个具有代表性的居住建筑样本，采用GIS分析与数理统计相结合的方式，在居住建筑1800m半径范围内，以200m为间隔建立缓冲区，开展全年、最冷月份能耗期及最热月份能耗期建筑能耗与周边空间形态关联性的连续尺度研究。结果表明：①道路密度、开发密度、容积率均与建筑能耗呈正相关，但各指标对应的能耗时期、尺度范围有较大差异。②水面率、土地利用混合度是与建筑能耗相关的共性指标，但在不同地区的正负效应及尺度范围不同；③影响浙闽地区县域城镇最热月份能耗期能耗的关键形态因子为1000m-道路密度、800m-水面率、1600m-开放空间率；影响连江最冷月份能耗期能耗的关键形态因子为800m-水面率。④就控碳单元设置而言，长兴县可重点关注1000-1200m半径范围，连江县可重点关注800-1000m、1400m半径范围。⑤浙闽地区县域城镇特性指标为开放空间率，与最热月份能耗期建筑能呈负相关；长兴县特性指标为容积率、水岸密度，分别与全年、最冷月份能耗期及最热月份能耗期建筑能耗呈正、负相关。据此提出3个低碳城镇规划建议，为实现我国浙闽地区县域城镇低碳转型、居住建筑节能减排提供理论依据。 Abstract:Since the energy consumption of residential buildings is directly affected by the spatial form surrounding the buildings, determining the low-carbon effect of the spatial shape and its optimal scale contributes to realizing the low-carbon transformation of towns and township.Taking Changxing County of Zhejiang Province and Lianjiang County of Fujian Province for example, 49 representative samples of residential buildings are selected in total and included in this research. Based on both GIS analysis and the method of mathematical statistics, with buffer zones built at the interval of 200m within a radius of 1800m around the residential buildings, a continuous-scale research is conducted on the correlation between building energy consumption and the spatial pattern around throughout the year, in the consumption period of the coldest month and in the consumption period of the hottest month.The results showed that:①road density, development density, and floor area ratio were all positively correlated with building energy consumption, but the energy consumption period and scale range corresponding to each index were quite different. ②Water surface ratio and land use mixing degree were common indicators related to building energy consumption, but their positive and negative effects and scale ranges vary in different regions. ③The key form factors that affect the energy consumption of towns in Zhejiang and Fujian during the monthly energy consumption period with the highest temperature were 1000 m- road density, 800 m- water surface ratio, and 1600 m- open space rate; The key form factor that affects the energy consumption during the energy consumption period of the lowest temperature month in Lianjiang is 800 m-water surface ratio. ④In the selection of carbon control unit, it is necessary for Changxing County to focus on the radius range of 1000 -1200 m, and Lianjiang County to focus on the radius range of 800 -1000 m and 1400 m.⑤ Characteristic indexes of towns in counties in Zhejiang and Fujian were the rate of open space, which was negatively correlated to building energy consumption in the consumption period of the hottest month; characteristic indexes of Changxing County are floor area ratio and waterfront density, which were positively and negatively correlated to building energy consumption throughout the year, in the consumption period of the coldest month and in the consumption period of the hottest month respectively. On such a basis, 3 proposals concerning low carbon urban planning are made herein, which may serve as a theoretical basis for the low carbon transition of county towns and energy saving and emission reduction in residential buildings in Zhejiang Province and Fujian Province. 参考文献 相似文献 引证文献

Exploring the effect of urban sprawl on carbon dioxide emissions: An urban sprawl model analysis from remotely sensed nighttime light data

The easily and effectively exploration of the effect of urban sprawl on carbon dioxide emissions (EUC) is highly important for developing a sustainable urban spatial structure and a low-carbon urban planning system in a country. However, the comprehensive understanding of EUC and its transmission factors remain inadequate because previous studies were mainly concentrated on a single city, stage, and region based on the statistical data and traditional optical remotely sensed images. Thus, taking 41 typical cities in Southwest China as examples, an urban sprawl (US) model was developed for quantifying US from remotely sensed nighttime light data. The degree of EUC were then verified in different city sizes, stages, and regions. The transmission factors of EUC were further evaluated. Results show that US can aggravate carbon dioxide emissions (CE), and the robust analysis further confirms the positive effect of US on CE. US in small cities has the strongest effect on CE, whereas that in medium cities has the weakest effect. In addition, EUC is greater in pre-stage (2000–2009) than that in post-stage (2010–2018). EUC in Guizhou, Yunnan, and Sichuan–Chongqing gradually decreases. The transmission factor results indicate that US can aggravate CE through the urban transport, construction industry, and urban heat island. It is suggested that more attention should be paid to US environmental problems with China's future urban development.

Intelligent Mining of Urban Ventilation Corridors Based on High-Precision Oblique Photographic Images.

With the advancement of urbanization and the impact of industrial pollution, the issue of urban ventilation has attracted increasing attention. Research on urban ventilation corridors is a hotspot in the field of urban planning. Traditional studies on ventilation corridors mostly focus on qualitative or simulated research on urban climate issues such as the intensified urban heat island effect, serious environmental pollution, and insufficient climate adaptability. Based on the high-precision urban remote sensing image data obtained by aeromagnetic oblique photography, this paper calculates the frontal area density of the city with reference to the urban wind statistics. Based on the existing urban patterns, template matching technology was used to automatically excavate urban ventilation corridors, which provides scientific and reasonable algorithmic support for the rapid construction of potential urban ventilation corridor paths. It also provides technical methods and decision basis for low-carbon urban planning, ecological planning and microclimate optimization design. This method was proved to be effective through experiments in Deqing city, Zhejiang Province, China.

A Multiscale Evaluation of the Coupling Relationship between Urban Land and Carbon Emissions: A Case Study of Chongqing, China

Exploring the coupling relationship between urban land and carbon emissions (CE) is one of the important premises for coordinating the urban development and the ecological environment. Due to the influence of the scale effect, a systematic evaluation of the CE at different scales will help to develop more reasonable strategies for low-carbon urban planning. However, corresponding studies are still lacking. Hence, two administrative scales (e.g., region and county) in Chongqing were selected as experimental objects to compare and analyze the CE at different scales using the spatiotemporal coupling and coupling coordination models. The results show that urban land and carbon emissions presented a significant growth trend in Chongqing at different scales from 2000 to 2015. The strength of the spatiotemporal coupling relationship between urban land and total carbon emissions gradually increased with increasing scale. At the regional scale, the high coupling coordination between urban land and total carbon emissions was mainly concentrated in the urban functional development region. Additionally, the high coupling coordination between urban land and carbon emission intensity (OI) was still located in the counties within the metropolitan region of Chongqing, but the low OI was mainly distributed in the counties in the northeastern and southeastern regions of Chongqing at the county level. This study illustrates the multiscale trend of CE and suggests differentiated urban land and carbon emission reduction policies for controlling urban land sprawl and reducing carbon emissions.

Urban Design Strategy from the Perspective of Low-Carbon City Planning

<p>The economy growth has improved the development of cities. In cities' continuous development and construction process, carbon emissions are also gradually increasing, causing serious environmental pollution and energy shortage. At present, low-carbon urban planning and design has become the demand of contemporary urban construction, and sustainable low-carbon economy has become the inevitable choice of urban planning. Based on this, this article briefly introduces the concept of low-carbon city planning and the principles of urban design from the perspective of low-carbon city. By analyzing the existing problems in current urban planning, this article proposes urban design strategies from the view of low-carbon city planning, seeking to make contributions to the improvement of urban planning levels.</p>

Planeación de bajo carbono de megalópolis en México

El objetivo del artículo es identificar los desafíos principales para la planeación urbana de bajo carbono en México a partir del reconocimiento de los efectos principales del cambio climático en los centros de población de la megalópolis del centro de México y de la revisión de los avances parciales en materia de Programas Estatales para la Acción ante el Cambio Climático de los Estados de la República Mexicana. Se hace una propuesta inicial de estrategias para la planeación sustentable de ciudades de bajo carbono que integra los diversos usos del suelo urbano con algunos de tipo rural y se concluye que dicha propuesta podría servir como punto de partida para coadyuvar al reordenamiento en materia urbana y rural en las zonas urbanas de mayores conflictos que, a juicio de los autores, son las megalópolis y, así, vislumbrar soluciones para el mejoramiento del medio ambiente.

Smart solutions shape for sustainable low-carbon future: A review on smart cities and industrial parks in China

To promote sustainable urban development and green industrial process are critical solutions for sustainable and low-carbon society transition in China, considering the significant environmental impacts derived from the industrialization and surging urbanization. Under this background, China adopts top-down programs on smart cities and smart industrial parks to forward the above efforts. While practices and lessons from these programs will be valuable to enlighten other regions and practitioners, to date, rather few studies have paid attentions to this issue. Particularly, the emerging smart technologies strongly support the practice, via offering smart solutions like better renewable energy projection, low-carbon life styles transformation, as well as energy planning and management. However, there has been a lack of discussing their future role in-depth. With this circumstance, this paper conducts an integrated and in-depth review on China's promotion on smart cities and smart industrial parks. In detail, the national pilots, key technical innovations, incentives and policies framework, as well as spatial features were discussed in-depth. Particularly, we further explored how the smart solutions can contribute to better decision making on low-carbon urban and industrial system planning. Finally, we highlighted policy recommendations targeting on future smart cities and industrial parks promotion, focusing on the perspectives of technological and social system innovations, innovative decision support tools, and smart management framework. Our results expect to offer critical enlightenments for policy makers to address future concerns on smart cities and industrial parks promotion and management.