• Energy Research

L'élaboration de politiques pour une ville à faibles émissions de carbone nécessite une compréhension approfondie des caractéristiques du mode de vie et de la consommation résidentiels urbains. L'approche comptable basée sur la production basée sur des données statistiques descendantes a une capacité limitée à refléter les émissions totales de gaz à effet de serre (GES) provenant de la consommation résidentielle. Dans cet article, nous présentons une méthodologie de comptabilisation des émissions de GES basée sur une enquête pour la consommation résidentielle urbaine, et l'appliquons dans la ville de Xiamen, une ville côtière en urbanisation rapide dans le sud-est de la Chine. Sur cette base, les principaux facteurs d'influence déterminant les émissions de GES résidentielles à l'échelle des ménages et des communautés sont identifiés, et les profils typiques des ménages et des communautés à émissions de GES faibles, moyennes et élevées sont identifiés. Jusqu'à 70 % des émissions de GES des ménages proviennent d'activités régionales et nationales qui soutiennent la consommation des ménages, y compris la fourniture d'énergie et de matériaux de construction, tandis que 17 % proviennent de services et de fournitures de base au niveau urbain tels que le traitement des eaux usées et la gestion des déchets solides, et seulement 13 % sont des émissions directes provenant de la consommation des ménages. La superficie des logements et la taille des ménages sont les deux principaux facteurs déterminant les émissions de GES provenant de la consommation résidentielle à l'échelle des ménages, tandis que la superficie moyenne des logements et la hauteur des bâtiments étaient les principaux facteurs à l'échelle communautaire. Nos résultats montrent une grande disparité dans les profils d'émissions de GES entre les différents ménages, les ménages à fortes émissions de GES émettant environ cinq fois plus que les ménages à faibles émissions de GES. Les émissions des communautés à fortes émissions de GES sont environ deux fois plus élevées que celles des communautés à faibles émissions de GES. Nos résultats peuvent contribuer à des politiques mieux adaptées et ciblées visant à réduire les émissions de GES des ménages et à développer des communautés résidentielles à faibles émissions de GES en Chine. Diseñar políticas para una ciudad baja en carbono requiere una comprensión cuidadosa de las características del estilo de vida y el consumo residencial urbano. El enfoque de contabilidad basada en la producción basado en datos estadísticos de arriba hacia abajo tiene una capacidad limitada para reflejar las emisiones totales de gases de efecto invernadero (GEI) del consumo residencial. En este documento, presentamos una metodología de contabilidad de emisiones de GEI basada en encuestas para el consumo residencial urbano, y la aplicamos en la ciudad de Xiamen, una ciudad costera en rápida urbanización en el sureste de China. Con base en esto, se identifican los principales factores que determinan las emisiones residenciales de GEI a escala doméstica y comunitaria, y se identifican los perfiles típicos de los hogares y comunidades de bajas, medias y altas emisiones de GEI. Hasta el 70% de las emisiones de GEI de los hogares provienen de actividades regionales y nacionales que apoyan el consumo doméstico, incluido el suministro de energía y materiales de construcción, mientras que el 17% proviene de servicios y suministros básicos a nivel urbano, como el tratamiento de aguas residuales y la gestión de residuos sólidos, y solo el 13% son emisiones directas del consumo doméstico. El área de la vivienda y el tamaño del hogar son los dos factores principales que determinan las emisiones de GEI del consumo residencial a escala del hogar, mientras que el área promedio de la vivienda y la altura del edificio fueron los principales factores a escala de la comunidad. Nuestros resultados muestran una gran disparidad en los perfiles de emisiones de GEI entre diferentes hogares, con hogares con altas emisiones de GEI que emiten aproximadamente cinco veces más que los hogares con bajas emisiones de GEI. Las emisiones de las comunidades con altas emisiones de GEI son aproximadamente el doble que las de las comunidades con bajas emisiones de GEI. Nuestros hallazgos pueden contribuir a políticas mejor adaptadas y específicas destinadas a reducir las emisiones de GEI de los hogares y a desarrollar comunidades residenciales con bajas emisiones de GEI en China. Devising policies for a low carbon city requires a careful understanding of the characteristics of urban residential lifestyle and consumption. The production-based accounting approach based on top-down statistical data has a limited ability to reflect the total greenhouse gas (GHG) emissions from residential consumption. In this paper, we present a survey-based GHG emissions accounting methodology for urban residential consumption, and apply it in Xiamen City, a rapidly urbanizing coastal city in southeast China. Based on this, the main influencing factors determining residential GHG emissions at the household and community scale are identified, and the typical profiles of low, medium and high GHG emission households and communities are identified. Up to 70% of household GHG emissions are from regional and national activities that support household consumption including the supply of energy and building materials, while 17% are from urban level basic services and supplies such as sewage treatment and solid waste management, and only 13% are direct emissions from household consumption. Housing area and household size are the two main factors determining GHG emissions from residential consumption at the household scale, while average housing area and building height were the main factors at the community scale. Our results show a large disparity in GHG emissions profiles among different households, with high GHG emissions households emitting about five times more than low GHG emissions households. Emissions from high GHG emissions communities are about twice as high as from low GHG emissions communities. Our findings can contribute to better tailored and targeted policies aimed at reducing household GHG emissions, and developing low GHG emissions residential communities in China. يتطلب وضع سياسات لمدينة منخفضة الكربون فهمًا دقيقًا لخصائص نمط الحياة السكنية الحضرية والاستهلاك. يتمتع نهج المحاسبة القائم على الإنتاج القائم على البيانات الإحصائية التنازلية بقدرة محدودة على عكس إجمالي انبعاثات غازات الدفيئة من الاستهلاك السكني. في هذه الورقة، نقدم منهجية حساب انبعاثات غازات الدفيئة القائمة على المسح للاستهلاك السكني الحضري، ونطبقها في مدينة شيامن، وهي مدينة ساحلية سريعة التحضر في جنوب شرق الصين. بناءً على ذلك، يتم تحديد العوامل المؤثرة الرئيسية التي تحدد انبعاثات غازات الدفيئة السكنية على مستوى الأسرة والمجتمع، ويتم تحديد الملامح النموذجية للأسر والمجتمعات ذات الانبعاثات المنخفضة والمتوسطة والعالية من غازات الدفيئة. ما يصل إلى 70 ٪ من انبعاثات غازات الدفيئة المنزلية هي من الأنشطة الإقليمية والوطنية التي تدعم الاستهلاك المنزلي بما في ذلك إمدادات الطاقة ومواد البناء، في حين أن 17 ٪ من الخدمات والإمدادات الأساسية على المستوى الحضري مثل معالجة مياه الصرف الصحي وإدارة النفايات الصلبة، و 13 ٪ فقط هي انبعاثات مباشرة من الاستهلاك المنزلي. منطقة السكن وحجم الأسرة هما العاملان الرئيسيان اللذان يحددان انبعاثات غازات الدفيئة من الاستهلاك السكني على نطاق الأسرة، في حين كان متوسط مساحة السكن وارتفاع المبنى هما العاملان الرئيسيان على نطاق المجتمع. تُظهر نتائجنا تفاوتًا كبيرًا في ملامح انبعاثات غازات الدفيئة بين الأسر المختلفة، حيث تنبعث من الأسر ذات انبعاثات غازات الدفيئة المرتفعة حوالي خمسة أضعاف انبعاثات الأسر ذات انبعاثات غازات الدفيئة المنخفضة. تبلغ الانبعاثات من المجتمعات ذات الانبعاثات العالية من غازات الدفيئة حوالي ضعف الانبعاثات من المجتمعات ذات الانبعاثات المنخفضة من غازات الدفيئة. يمكن أن تسهم النتائج التي توصلنا إليها في تحسين السياسات المصممة والمستهدفة التي تهدف إلى الحد من انبعاثات غازات الدفيئة المنزلية، وتطوير مجتمعات سكنية منخفضة انبعاثات غازات الدفيئة في الصين.

Unbalanced regional development is widespread, and the imbalance of regional development in developing countries with rapid urbanization is increasingly apparent. This threatens the sustainable development of the region. Promoting the coordinated development of the region has become a hot spot of scientific research and a major practical need. Taking 99 counties of Jiangsu Province China, a typical coastal plain region, as the basic research unit, this paper explores the unbalanced development characteristics of the regional urban spatial form using three indicators: urban spatial expansion size, development intensity, and distribution aggregation degree. Then, their driving mechanisms were evaluated using spatial autocorrelation analysis, Pearson correlation analysis, linear regression, and geographically weighted regression. Our results found that the areas with larger urban spatial expansion size and development intensity were mainly concentrated in southern Jiangsu, where there was a positive spatial correlation between them. We found no agglomeration phenomenon in urban spatial distribution aggregation degree. From the perspective of driving factors: economics was the main driving factor of urban spatial expansion size; urbanization level and urbanization quality were the main driving factors of urban spatial development intensity. Natural landform and urbanization level are the main driving factors of urban spatial distribution aggregation degree. Finally, we discussed the optimization strategy of regional coordinated development. The quality of urbanization development and regional integration should be promoted in Southern Jiangsu. The level of urbanization development should be improved relying on rapid transportation to develop along the axis in central Jiangsu. The economic size should be increased, focusing on the expansion of the urban agglomeration in northern Jiangsu. This study will enrich the perspective of research on the characteristics and mechanisms of regional urban spatial imbalance, and helps to optimize and regulate the imbalance of regional urban development from multiple perspectives.

Urban sprawl has led to various economic, social, and environmental problems. Therefore, it is very significant to improve the efficiency of resource usage and promote the development of compact urban form. It is a common topic that measuring urban compactness is done with certain ways and methods as well. Presently, most urban compactness measurement methods are based on two-dimensional (2D) formats, but methods based on three-dimensional (3D) formats that can precisely describe the actual urban spatial conditions are still lacking. To measure the compactness of the 3D urban spatial form accurately, a 3D Compactness Index (VCI) was established based on the Law of Gravitation and the quantitative measurement model. In this model, larger 3D Compactness Index values indicate a more 3D-compact city. However, different urban scales may influence the discrepancy scale of different cities. Thus, the 3D Compactness Index model was normalized as the Normalized 3D Compactness Index (NVCI) to eliminate such discrepancies. In the Normalized 3D Compactness Index model, a sphere with the same volume of real urban buildings in the city was assumed as the most compact 3D urban form, and which was also calculated by 3D Compactness Index processing. The compactness value of the normalized 3D urban form is obtained by comparing the 3D Compactness Index with the most compact 3D urban form. In this study, 1149 typical communities in Xiamen, China, were selected as the experimental fields to verify the index. Some of communities have a quite different Normalized 3D Compactness Index, although they have a similar Normalized 2D Compactness Index (NCI), respectively. Moreover, comparing with the 2D Compactness Index (CI) and Normalized 2D Compactness Index (NCI), the 3D Compactness Index and Normalized 3D Compactness Index can describe and explain reality more precisely. The constructed 3D urban compactness model is expected to contribute to scientific study on urban compactness.

Residential spatial differentiation, also called residential segregation, is a representation of the differentiation of social stratum in economic income, social status, education degree, lifestyle, and other aspects, based on an urban geographical space. In this paper, Xiamen Island is taken as example to calculate the dissimilarity index and the multi-group dissimilarity index at three scales (districts, sub-districts, and communities) by using the land area, population size, and green space area of different housing types. The characteristics of residential differentiation are analyzed. It is found that both spatial differentiation and multi-group spatial differentiation have significant scale effects. The smaller the scale of the spatial statistics unit, the larger the spatial differentiation and multi-group spatial differentiation. Significant differences are found in residential differentiation among different housing types. The residential differentiation is, not only demonstrated in land area and population size, but also in the resources of green space. More importantly, a balanced allocation of green space will help to reduce the degree of residential differentiation. With urban expansion and social-economic development, residential spatial differentiation will likely change. An understanding of residential differentiation is a guide for urban master planning and detailed regulatory planning. It will help to promote social harmonious development and urban sustainable development by the reasonable configuration of land and resources.

Against the backdrop of rapid population aging and significant demographic changes, cities are facing great challenges in providing healthcare resources for the elderly. Understanding the equity and spatial differentiation mechanisms between the demand and supply of healthcare resources for the elderly in cities, especially in rapidly urbanized areas, can help policymakers to promote the planning and construction of a ‘Just City’ and ‘Age-Friendly City’, safeguarding the human well-being of the elderly. This study combines outpatient appointment data classified by service type with community-scale demographic data to assess the equity in the supply of medical resources for the elderly in the city using a refined two-step moving search method. Taking Xiamen City as an example, we analyzed the spatial heterogeneity in the equity in the supply of different types of health care services for potential elderly patients in each community in the city. Then, we selected six driving factors to explore the underlying driving factors. The results showed that there were 308 communities (61.11%) with a high medical service balance of the internal medicine type for the elderly in Xiamen City, concentrated in the southwest of the whole area. There are only 157 communities with high medical service balance for the surgical type of medical services, concentrated in the central part and southwest of Xiamen City. For these two types of medical services, the number of communities with low medical service balance is 30 (5.95%) and 182 (36.11%), respectively. These communities are far away from the central city. Economic vitality is the most important driving factor in the spatial distribution pattern of equity in the balance of medical services for the elderly. Our study can provide quantitative information on the current situation evaluation and decision support for the development and design of a ‘Just City’ and ‘Age-Friendly City’ Planning.

Abstract Carbon intensity targets, namely carbon emissions per unit of GDP, are used as macro-level indicators of low carbon performance at the province- and city-level in China. However, this measure is too aggregated to provide a meaningful indication of low carbon performance and inform practical management strategies. Most traditional low carbon city indicators have no direct relationship with national carbon intensity reduction targets and do not provide municipal government administrators with the practical information they need to inform low carbon development at the local level. This paper integrates city-level carbon intensity targets with a low carbon city indicator system by means of a decomposed method to offer a better approach for carbon intensity reduction performance evaluation. Using Xiamen as a case study, one of the NDRC's low-carbon project areas, a target integrated indicator system is presented, including indicator values which have been determined through scenario analysis and calculation. The indicators and values can help local municipal governments to meet their carbon intensity reduction targets by providing an indication of current performance and identifying sectors where there is scope for further improvement. The methodology provides the theoretical basis and reference values for the evaluation of a city's low carbon performance within the context of achieving a carbon reduction target, thereby enhancing the potential for scientific and operational evaluation at the local level.

Background: Achieving urban sustainability is the ultimate destination of urban development. City rankings as one of the sustainability assessment tools have received increasing attention from the scientific community. However, few study assesses Chinese cities’ sustainability performance using the big data of existing city rankings. Aim: This study aims to assess Chinese cities’ sustainability performances based on the outcomes of the existing internet big data of city rankings. Methods: The outcomes of city rankings were used as the raw dataset. The “sustainability” of city rankings, city’s appearance frequency, and its ranking place were comprehensively considered during evaluation processes. By considering the above factors, the scores of different cities were calculated in terms of overall sustainability and domain sustainability. Furthermore, the GeoDetector was applied to explore the association between social-economic and overall ranking scores as well as the interrelation among TBL dimensions. Results: Chinese cities’ sustainability performance was extremely uneven in spatial distribution. In terms of overall and domain sustainability, well-performing cities were aggregated in the Beijing-Tianjin-Hebei, the Yangtze River Delta, and the Pearl River Delta metropolitan regions. The top ten sustainable cities were Hangzhou, Beijing, Shenzhen, Guangzhou, Zhuhai, Hong Kong, Tianjin, Suzhou, and Xiamen. Most cities did not reach good coordination among the TBL dimensions, instead of developing well in one or two aspects. The results also revealed that current city rankings eyeing more economic and social development, while considering less environmental dimension. Moreover, TBL dimensions mutually reinforce each other in sustainable city construction. The environmental pillar played a critical role and interacting with other dimensions significantly enhanced urban sustainability. Conclusion: The outcomes of existing city rankings can be used as a new resource to evaluate cities’ sustainability performance. Current city rankings in China are not systematically considered in terms of TBL dimensions. Cities should enhance the coordination among TBL pillars, and increase the attention on environmental dimension. More empirical studies involving big data of city rankings will contribute to a new perspective to promote the practice of sustainable urbanization in China.